Higher proportion wind power penetration has great impact on grid operation and dispatching,
intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm
of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network)
is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural
network training sample, PSO-BP (particle swarm combined with back propagation neural network) is
employed on prediction error dynamic revision. From the angle of root mean square error (RMSE), the
mean absolute error (MAE) and convergence rate, analysis and comparison of several intelligent
algorithms (BP, RBP, PSO-BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the
proposed prediction method. Further, due to the physical function of energy storage in improving accuracy
of schedule pre-fabrication, a mathematical statistical method is proposed to determine the optimal
capacity of the storage batteries in power forecasting based on the historical statistical data of wind farm.
Algorithm feasibility is validated by application of experiment simulation and comparative analysis.
A WIND POWER PREDICTION METHOD BASED ON BAYESIAN FUSIONcsandit
The document proposes a wind power prediction method based on Bayesian fusion of multiple numerical weather predictions. It first establishes a relationship between wind speed and power using neural networks. It then analyzes the characteristics of wind speed forecasts from three independent weather sources. A Bayesian method is designed to fuse the wind speed forecasts, yielding a more accurate prediction than any single source. The fused wind speed is input to the neural network model to predict wind power at 15-minute intervals. Experimental results show the method improves accuracy of wind speed and power forecasting compared to using a single source.
In this paper, a new technique has been proposed to solve the trade off common problem in hill climbing search algorithm (HCS) to reach maximum power point tracking (MPPT). The main aim of the new technique is to increase the power efficiency for the wind energy conversion system (WECS). The proposed technique has been combined the three-mode algorithm to be simpler. The novel algorithm is increasing the ability to reach the MPPT without delay. The novel algorithm shows fast tracking capability and enhanced stability under change wind speed conditions.
This document summarizes a study on the impact of field roughness, power losses, and turbulence intensity on electricity production for an onshore wind farm in Kitka, Kosovo. The study analyzed wind data collected from an onsite met mast from August to December 2017. It estimated annual energy production for the wind farm using wake and loss models. Turbulence intensity was estimated to be 9-12% at hub height based on wind speed data. The roughness of the terrain was found to be less than the added roughness of wind turbines. Despite differences in elevation between turbines, the site roughness index was found to be mostly consistent, allowing similar turbines to be installed without affecting energy production quality.
The document discusses various techniques for mathematically modeling wind turbine power curves. It begins by explaining the basic components and equations for wind energy conversion. It then describes factors that influence power output like wind speed distribution and tower height. Methods are classified as parametric (using equations) or non-parametric (no assumptions). Parametric techniques include linear segmented models, polynomials, and logistic functions. Non-parametric techniques involve cubic spline interpolation, neural networks, fuzzy methods, and copula models. Accurately modeling power curves is important for wind farm optimization and energy forecasting.
IRJET- A Review of MPPT Algorithms Employed in Wind Energy Conversion SystemIRJET Journal
This document reviews and compares various maximum power point tracking (MPPT) algorithms used in wind energy conversion systems. It begins with an introduction to wind energy and the need for MPPT algorithms due to the intermittent nature of wind speed. It then categorizes and describes MPPT algorithms based on whether they use direct power control (DPC) or indirect power control (IPC). DPC algorithms like hill climb search directly perturb control variables while IPC algorithms use relationships between indirect parameters. The document provides details on specific algorithms like tip speed ratio, optimal torque, perturbation and observation. It concludes by discussing hybrid and intelligent control methods like fuzzy logic and noting that each algorithm has tradeoffs in complexity, sensor requirements, and performance.
Power Flow Control in Grid-Connected Wind Energy Conversion System Using PMSG...IOSR Journals
This document summarizes a research paper that proposes a new method for reducing grid current total harmonic distortion in a wind energy conversion system using a permanent magnet synchronous generator. The method uses optimal torque control for maximum power point tracking from the wind turbines. It then employs vector control of the grid-side inverter to both control active power injection into the grid and eliminate higher-order current harmonics from local nonlinear loads, improving power quality. Simulation results demonstrate the benefits of maximum power point tracking at different wind speeds and lower total harmonic distortion when the harmonic elimination function is used.
The document discusses wind speed prediction using the Weibull distribution and a hybrid Weibull-ANN technique. It presents the motivation for improved wind speed prediction due to the increasing use of wind energy. The Weibull distribution is described as a common statistical model used to analyze wind speed data. An artificial neural network model with backpropagation is also introduced for prediction. The document then analyzes wind speed data from Bhubaneswar using Weibull distributions and histograms to model the data distributions. Finally, it evaluates the hybrid Weibull-ANN technique for wind speed prediction performance.
IRJET- Implementation of Conventional Perturb with different Load for Maximum...IRJET Journal
This document discusses the implementation of a modified perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for a photovoltaic system using a microcontroller. The traditional P&O algorithm is simple but has issues during rapid changes in irradiance/load, including oscillating around the MPP or moving away from it. The proposed algorithm adds a constant load method to help the traditional P&O algorithm identify the cause of power changes and make better decisions during initial perturbations. Simulation and experimental results show the proposed algorithm performs better than the traditional P&O approach.
A WIND POWER PREDICTION METHOD BASED ON BAYESIAN FUSIONcsandit
The document proposes a wind power prediction method based on Bayesian fusion of multiple numerical weather predictions. It first establishes a relationship between wind speed and power using neural networks. It then analyzes the characteristics of wind speed forecasts from three independent weather sources. A Bayesian method is designed to fuse the wind speed forecasts, yielding a more accurate prediction than any single source. The fused wind speed is input to the neural network model to predict wind power at 15-minute intervals. Experimental results show the method improves accuracy of wind speed and power forecasting compared to using a single source.
In this paper, a new technique has been proposed to solve the trade off common problem in hill climbing search algorithm (HCS) to reach maximum power point tracking (MPPT). The main aim of the new technique is to increase the power efficiency for the wind energy conversion system (WECS). The proposed technique has been combined the three-mode algorithm to be simpler. The novel algorithm is increasing the ability to reach the MPPT without delay. The novel algorithm shows fast tracking capability and enhanced stability under change wind speed conditions.
This document summarizes a study on the impact of field roughness, power losses, and turbulence intensity on electricity production for an onshore wind farm in Kitka, Kosovo. The study analyzed wind data collected from an onsite met mast from August to December 2017. It estimated annual energy production for the wind farm using wake and loss models. Turbulence intensity was estimated to be 9-12% at hub height based on wind speed data. The roughness of the terrain was found to be less than the added roughness of wind turbines. Despite differences in elevation between turbines, the site roughness index was found to be mostly consistent, allowing similar turbines to be installed without affecting energy production quality.
The document discusses various techniques for mathematically modeling wind turbine power curves. It begins by explaining the basic components and equations for wind energy conversion. It then describes factors that influence power output like wind speed distribution and tower height. Methods are classified as parametric (using equations) or non-parametric (no assumptions). Parametric techniques include linear segmented models, polynomials, and logistic functions. Non-parametric techniques involve cubic spline interpolation, neural networks, fuzzy methods, and copula models. Accurately modeling power curves is important for wind farm optimization and energy forecasting.
IRJET- A Review of MPPT Algorithms Employed in Wind Energy Conversion SystemIRJET Journal
This document reviews and compares various maximum power point tracking (MPPT) algorithms used in wind energy conversion systems. It begins with an introduction to wind energy and the need for MPPT algorithms due to the intermittent nature of wind speed. It then categorizes and describes MPPT algorithms based on whether they use direct power control (DPC) or indirect power control (IPC). DPC algorithms like hill climb search directly perturb control variables while IPC algorithms use relationships between indirect parameters. The document provides details on specific algorithms like tip speed ratio, optimal torque, perturbation and observation. It concludes by discussing hybrid and intelligent control methods like fuzzy logic and noting that each algorithm has tradeoffs in complexity, sensor requirements, and performance.
Power Flow Control in Grid-Connected Wind Energy Conversion System Using PMSG...IOSR Journals
This document summarizes a research paper that proposes a new method for reducing grid current total harmonic distortion in a wind energy conversion system using a permanent magnet synchronous generator. The method uses optimal torque control for maximum power point tracking from the wind turbines. It then employs vector control of the grid-side inverter to both control active power injection into the grid and eliminate higher-order current harmonics from local nonlinear loads, improving power quality. Simulation results demonstrate the benefits of maximum power point tracking at different wind speeds and lower total harmonic distortion when the harmonic elimination function is used.
The document discusses wind speed prediction using the Weibull distribution and a hybrid Weibull-ANN technique. It presents the motivation for improved wind speed prediction due to the increasing use of wind energy. The Weibull distribution is described as a common statistical model used to analyze wind speed data. An artificial neural network model with backpropagation is also introduced for prediction. The document then analyzes wind speed data from Bhubaneswar using Weibull distributions and histograms to model the data distributions. Finally, it evaluates the hybrid Weibull-ANN technique for wind speed prediction performance.
IRJET- Implementation of Conventional Perturb with different Load for Maximum...IRJET Journal
This document discusses the implementation of a modified perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for a photovoltaic system using a microcontroller. The traditional P&O algorithm is simple but has issues during rapid changes in irradiance/load, including oscillating around the MPP or moving away from it. The proposed algorithm adds a constant load method to help the traditional P&O algorithm identify the cause of power changes and make better decisions during initial perturbations. Simulation and experimental results show the proposed algorithm performs better than the traditional P&O approach.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Application of swarm intelligence algorithms to energy management of prosumer...IJECEIAES
The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers.
Feasibility Study of a Grid Connected Hybrid Wind/PV SystemIJAPEJOURNAL
This paper investigates the feasibility of a grid connected, large-scale hybrid wind/PV system. From data available an area called RasElnaqab in Jordan is chosen because it enjoys both high average wind speed of 6.13 m/s and high average solar radiation of 5.9KWhr/m2 /day. MATLAB and HOMER software’s are used for sizing and economical analysis respectively. Results show that76124 SUNTECH PV panels and 38 GW87-1.5MW wind turbines are the optimal choice. The net present cost (NPC) is 130,115,936$, the cost of energy (COE) is 0.049$/KWhr with a renewable fraction of 74.1%.A stepby-step process to determine the optimal sizing of Hybrid Wind/PV system is presented and it can be applied anywhere.
Wind power forecasting: A Case Study in Terrain using Artificial IntelligenceIRJET Journal
This document presents a study on using artificial neural networks to forecast wind power. Real-time data on wind speed, direction, temperature, humidity and pressure was collected from a measurement station. 100 artificial neural networks with different structures were trained and tested. The best performing network was a multilayer perceptron with 6 inputs, 24 hidden layers, exponential activation and identity output activation. This network achieved a 99% success rate in estimating wind power compared to real measured data. The study demonstrates that artificial neural networks can accurately estimate wind power for short-term forecasting.
Improving the delivered power quality from WECS to the grid based on PMSG con...IJECEIAES
Renewable energy has become one of the most energy resources nowadays, especially, wind energy. It is important to implement more analysis and develop new control algorithms due to the rapid changes in the wind generators size and the power electronics development in wind energy applications. This paper proposes a grid-connected wind energy conversion system (WECS) control scheme using permanent magnet synchronous generator (PMSG). The model works to improve the delivered power quality and maximize its value. The system contained one controller on the grid side converter (GSC) and two simulation packages used to simulate this model, which were PSIM software package for simulating power circuit and power electronics converters, and MATLAB software package for simulating the controller on Simulink. It employed a meta-heuristic technique to fulfil this target effectively. Mine-blast algorithm (MBA) and harmony search optimization technique (HSO) were applied to the proposed method to get the best controller coefficient to ensure maximum power to the grid and minimize the overshoot and the steady state error for the different control signals. The comparison between the results of the MBA and the HSO showed that the MBA gave better results with the proposed system.
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
This document reviews various maximum power point tracking (MPPT) techniques for photovoltaic systems. It discusses 17 different MPPT techniques, comparing them based on their method (direct control, sampling, modulation), variables tracked (voltage, current), required circuitry (analog, digital), need for tuning, relative cost, and hardware complexity. The techniques range from simple hill-climbing methods like perturb and observe to more advanced intelligent techniques using fuzzy logic, neural networks, and particle swarm optimization. The document concludes that fuzzy logic and other hybrid/intelligent techniques provide good performance for rapidly changing temperature and irradiance conditions with fast response and less fluctuation, though they require more complex hardware.
Exploring the best method of forecasting for short term electrical energy demandMesut Günes
This study includes applications of forecasting models established on the data that contain the electrical power consumption of a specific region which are observed hourly. At the beginning of the research, basic information about the electrical power system and the forecasting methods are given and the situation is clarified. Trakya region in Turkey which is in European side of Turkey is selected as the target region. The data is composed of hourly observed electrical energy values for the whole year of 2005 and some months of 2006 and 2007 which is 23 months in total. Because the data is large enough and the aim of the research is to establish accurate forecasting models for short term forecasting, quantitative methods are used. For this region, forecasting methods are improved for the short term electrical energy consumption that is the next 12 hours of the last day of each months and the best fitted model is determined for each months. The best fitted models are applied to the data and the related results are discussed.
Advance Data Mining - Analysis and forecasting of power factor for optimum el...Shrikant Samarth
Task: Execute a research project using data mining techniques
Approach: The topic chosen was ‘Analysis and Forecasting of Power Factor for Optimum Electric Consumption in a Household.’ Research question – What can be the best short term range of forecast for power factor patterns so that optimum energy consumption can be achieved for a household?
To answer the question, CRISM- DM method was used. The ARIMA machine learning model was developed using R.
Findings: The best short term range of forecasts for the power factor was achieved for 6 months and 12 months duration using the ARIMA model. The MAPE value for the ARIMA model was around 1.83.
Tools: Rstudio
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco IJECEIAES
This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, c (m/s) and shape parameter, k. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.
2018 solar energy forecasting based on hybrid neural network and improved met...Souvik Ganguli
The document proposes a new forecasting approach for solar power based on a hybrid neural network and improved metaheuristic algorithm. The forecasting engine uses a 3-stage neural network structure with the neural networks connected in series. The neural networks are first trained with Levenberg-Marquardt learning, and then the weights are further optimized by an improved shark smell optimization algorithm to avoid local minima. Test results on a real-world case show the proposed approach provides more accurate predictions of solar power compared to other methods.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Wind Power Density Analysis for Micro-Scale Wind Turbinestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
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This document discusses calculating the potential wind power in Indonesia using a high altitude wind energy (HAWE) method. Wind speed data from South Bone Bay and Aru Island were obtained from satellite imagery. Simulations showed that increasing the turbine height from 10 meters to 400 meters using balloons increased the average power by 2.2 times due to higher wind speeds at altitude. The results indicate HAWE has potential in Indonesia to access higher wind speeds and distribute power to remote areas using movable systems.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
2018 4th International Conference on Green Technology and Sust.docxlorainedeserre
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i ...
2018 4th International Conference on Green Technology and Sust.docxRAJU852744
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i.
This document summarizes a research paper that proposes using a battery energy storage system (BESS) at the point of common coupling for a wind farm to provide continuous power output. It presents a methodology to determine the optimal BESS capacity needed to balance the intermittent power from the wind farm and maintain a constant output. The paper models the wind power profile, develops control algorithms for the converters, sizes the BESS based on the maximum power and energy needed over time, and simulates the system in MATLAB/Simulink. The results demonstrate that the BESS is able to smooth fluctuations and provide continuous power without disturbing the grid.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Application of swarm intelligence algorithms to energy management of prosumer...IJECEIAES
The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers.
Feasibility Study of a Grid Connected Hybrid Wind/PV SystemIJAPEJOURNAL
This paper investigates the feasibility of a grid connected, large-scale hybrid wind/PV system. From data available an area called RasElnaqab in Jordan is chosen because it enjoys both high average wind speed of 6.13 m/s and high average solar radiation of 5.9KWhr/m2 /day. MATLAB and HOMER software’s are used for sizing and economical analysis respectively. Results show that76124 SUNTECH PV panels and 38 GW87-1.5MW wind turbines are the optimal choice. The net present cost (NPC) is 130,115,936$, the cost of energy (COE) is 0.049$/KWhr with a renewable fraction of 74.1%.A stepby-step process to determine the optimal sizing of Hybrid Wind/PV system is presented and it can be applied anywhere.
Wind power forecasting: A Case Study in Terrain using Artificial IntelligenceIRJET Journal
This document presents a study on using artificial neural networks to forecast wind power. Real-time data on wind speed, direction, temperature, humidity and pressure was collected from a measurement station. 100 artificial neural networks with different structures were trained and tested. The best performing network was a multilayer perceptron with 6 inputs, 24 hidden layers, exponential activation and identity output activation. This network achieved a 99% success rate in estimating wind power compared to real measured data. The study demonstrates that artificial neural networks can accurately estimate wind power for short-term forecasting.
Improving the delivered power quality from WECS to the grid based on PMSG con...IJECEIAES
Renewable energy has become one of the most energy resources nowadays, especially, wind energy. It is important to implement more analysis and develop new control algorithms due to the rapid changes in the wind generators size and the power electronics development in wind energy applications. This paper proposes a grid-connected wind energy conversion system (WECS) control scheme using permanent magnet synchronous generator (PMSG). The model works to improve the delivered power quality and maximize its value. The system contained one controller on the grid side converter (GSC) and two simulation packages used to simulate this model, which were PSIM software package for simulating power circuit and power electronics converters, and MATLAB software package for simulating the controller on Simulink. It employed a meta-heuristic technique to fulfil this target effectively. Mine-blast algorithm (MBA) and harmony search optimization technique (HSO) were applied to the proposed method to get the best controller coefficient to ensure maximum power to the grid and minimize the overshoot and the steady state error for the different control signals. The comparison between the results of the MBA and the HSO showed that the MBA gave better results with the proposed system.
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
This document reviews various maximum power point tracking (MPPT) techniques for photovoltaic systems. It discusses 17 different MPPT techniques, comparing them based on their method (direct control, sampling, modulation), variables tracked (voltage, current), required circuitry (analog, digital), need for tuning, relative cost, and hardware complexity. The techniques range from simple hill-climbing methods like perturb and observe to more advanced intelligent techniques using fuzzy logic, neural networks, and particle swarm optimization. The document concludes that fuzzy logic and other hybrid/intelligent techniques provide good performance for rapidly changing temperature and irradiance conditions with fast response and less fluctuation, though they require more complex hardware.
Exploring the best method of forecasting for short term electrical energy demandMesut Günes
This study includes applications of forecasting models established on the data that contain the electrical power consumption of a specific region which are observed hourly. At the beginning of the research, basic information about the electrical power system and the forecasting methods are given and the situation is clarified. Trakya region in Turkey which is in European side of Turkey is selected as the target region. The data is composed of hourly observed electrical energy values for the whole year of 2005 and some months of 2006 and 2007 which is 23 months in total. Because the data is large enough and the aim of the research is to establish accurate forecasting models for short term forecasting, quantitative methods are used. For this region, forecasting methods are improved for the short term electrical energy consumption that is the next 12 hours of the last day of each months and the best fitted model is determined for each months. The best fitted models are applied to the data and the related results are discussed.
Advance Data Mining - Analysis and forecasting of power factor for optimum el...Shrikant Samarth
Task: Execute a research project using data mining techniques
Approach: The topic chosen was ‘Analysis and Forecasting of Power Factor for Optimum Electric Consumption in a Household.’ Research question – What can be the best short term range of forecast for power factor patterns so that optimum energy consumption can be achieved for a household?
To answer the question, CRISM- DM method was used. The ARIMA machine learning model was developed using R.
Findings: The best short term range of forecasts for the power factor was achieved for 6 months and 12 months duration using the ARIMA model. The MAPE value for the ARIMA model was around 1.83.
Tools: Rstudio
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco IJECEIAES
This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, c (m/s) and shape parameter, k. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.
2018 solar energy forecasting based on hybrid neural network and improved met...Souvik Ganguli
The document proposes a new forecasting approach for solar power based on a hybrid neural network and improved metaheuristic algorithm. The forecasting engine uses a 3-stage neural network structure with the neural networks connected in series. The neural networks are first trained with Levenberg-Marquardt learning, and then the weights are further optimized by an improved shark smell optimization algorithm to avoid local minima. Test results on a real-world case show the proposed approach provides more accurate predictions of solar power compared to other methods.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Wind Power Density Analysis for Micro-Scale Wind Turbinestheijes
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This document discusses calculating the potential wind power in Indonesia using a high altitude wind energy (HAWE) method. Wind speed data from South Bone Bay and Aru Island were obtained from satellite imagery. Simulations showed that increasing the turbine height from 10 meters to 400 meters using balloons increased the average power by 2.2 times due to higher wind speeds at altitude. The results indicate HAWE has potential in Indonesia to access higher wind speeds and distribute power to remote areas using movable systems.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
2018 4th International Conference on Green Technology and Sust.docxlorainedeserre
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i ...
2018 4th International Conference on Green Technology and Sust.docxRAJU852744
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i.
This document summarizes a research paper that proposes using a battery energy storage system (BESS) at the point of common coupling for a wind farm to provide continuous power output. It presents a methodology to determine the optimal BESS capacity needed to balance the intermittent power from the wind farm and maintain a constant output. The paper models the wind power profile, develops control algorithms for the converters, sizes the BESS based on the maximum power and energy needed over time, and simulates the system in MATLAB/Simulink. The results demonstrate that the BESS is able to smooth fluctuations and provide continuous power without disturbing the grid.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
New typical power curves generation approach for accurate renewable distribut...IJECEIAES
This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.
The document discusses using optimized neural networks for short-term wind speed forecasting. It proposes using parametric recurrent neural networks (PRNNs) with an improved activation function that includes a logarithmic parameter "p" to optimize the network size. The PRNNs are trained to predict wind speed using historical wind farm data. Simulation results show the PRNNs more accurately predict wind speed up to 180 minutes in the future compared to numerical methods using polynomials. The value of the "p" parameter can identify linearly dependent neurons that can be combined to reduce the optimized network size.
Quantification of operating reserves with high penetration of wind power cons...IJECEIAES
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Dynamic responses improvement of grid connected wpgs using flc in high wind s...ijscmcj
Environmental and sustainability concerns are developing the significance of distributed generation (DG) based on renewable energy sources. In this paper, dynamic responses investigation of grid connected wind turbine using permanent magnet synchronous generator (PMSG) under variable wind speeds and load circumstances is carried out. In order to control of turbine output power using Fuzzy Logic controller (FLC) in comparison with PI controller is proposed. Furthermore, the pitch angle based on FLC using wind speed and active power as inputs, can have faster responses, thereby leading to smoother power curves, enhancement of dynamic performance of wind turbine and prevention of mechanical damages to PMSG. Inverter adjusted the DC link voltage and active power is fed by d-axis and reactive power is fed by q-axis (using P-Q control mode). Simulation of wind power generation system (WPGS) is carried out in Matlab/Simulink, and the results verify the correctness and feasibility of control strategy.
DYNAMIC RESPONSES IMPROVEMENT OF GRID CONNECTED WPGS USING FLC IN HIGH WIND S...ijscmcjournal
Environmental and sustainability concerns are developing the significance of distributed generation (DG) based on renewable energy sources. In this paper, dynamic responses investigation of grid connected wind turbine using permanent magnet synchronous generator (PMSG) under variable wind speeds and load circumstances is carried out. In order to control of turbine output power using Fuzzy Logic controller (FLC) in comparison with PI controller is proposed. Furthermore, the pitch angle based on FLC using wind speed and active power as inputs, can have faster responses, thereby leading to smoother power curves, enhancement of dynamic performance of wind turbine and prevention of mechanical damages to PMSG. Inverter adjusted the DC link voltage and active power is fed by d-axis and reactive power is fed by q-axis (using P-Q control mode). Simulation of wind power generation system (WPGS) is carried out in Matlab/Simulink, and the results verify the correctness and feasibility of control strategy.
This document summarizes a research paper that investigates improving the dynamic responses of grid-connected permanent magnet synchronous generator (PMSG) wind turbines using a fuzzy logic controller (FLC). The paper proposes using an FLC to control the pitch angle of the turbine blades based on wind speed and active power inputs. This allows for faster response compared to prior methods, leading to smoother power output and preventing mechanical damage. The system is modeled and simulated in Matlab/Simulink. Results show the FLC approach effectively regulates turbine output power under varying wind speeds and load conditions.
Coordination of blade pitch controller and battery energy storage using firef...TELKOMNIKA JOURNAL
Utilization of renewable energy sources (RESs) to generate electricity is increasing significantly in recent years due to global warming situation all over the world. Among RESs type, wind energy is becoming more favorable due to its sustainability and environmentally friendly characteristics. Although wind power system provides a promising solution to prevent global warming, they also contribute to the instability of the power system, especially in frequency stability due to uncertainty characteristic of the sources (wind speed). Hence, coordinated controller between blade pitch controller and battery energy storage (BES) system to enhance the frequency performance of wind power system is proposed in this work. Firefly algorithm (FA) is used as optimization method for achieving better coordination. From the investigated test systems, the frequency performance of wind power system can be increased by applying the proposed method. It is noticeable that by applying coordinated controller between blade pitch angle controller and battery energy storage using firefly algorithm the overshoot of the frequency can be reduced up to -0.2141 pu and accelerate the settling time up to 40.14 second.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo...IJECEIAES
The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
This document summarizes a research paper that proposes a new method for reducing grid current total harmonic distortion in a grid-connected wind energy conversion system using a permanent magnet synchronous generator. The method uses the grid-side inverter, which normally injects power into the grid, to also eliminate high-order harmonics generated by a nonlinear load connected to the grid. Simulation results on a system with a 20 kW permanent magnet synchronous generator show that when a nonlinear load is connected, the proposed harmonic elimination method significantly reduces the total harmonic distortion of the grid current compared to when it is not used. The maximum power point tracking algorithm employed also achieves over 97% tracking efficiency across different wind speeds tested.
In this paper, an adaptive anti-windup control strategy for permanent magnet synchronous generator dedicated for wind energy conversion systems. The proposed control has the advantage to suppress the performance deterioration caused by the overshooting phenomenon, and optimize the controller gains using the particle swarm optimization algorithm. The scheme of the speed controller is implemented on field orientation control in the generator side converter. A simulation of the proposed scheme is carried out in SIMULINK-MATLAB in order to evaluate the effectiveness of the control against the saturation and the parameter optimization.
Wind power prediction using a nonlinear autoregressive exogenous model netwo...IJECEIAES
The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
Voltage Compensation in Wind Power System using STATCOM Controlled by Soft Co...IJECEIAES
When severe voltage sags occur in weak power systemsassociated with gridconnected wind farms employing doubly fed induction generators, voltageinstability occurs, which may lead to forced disconnection of wind turbine.Shunt flexible AC transmission system devices like static synchronous compensator (STATCOM) may be harnessed to provide voltage support bydynamic injection of reactive power.In this work, the STATCOM providedvoltage compensation at the point of common coupling in five test cases,namely, simultaneous occurrence of step change (drop) in wind speed and dip in grid voltage, single line to ground, line to line, double line to groundfaults and sudden increment in load by more than a thousand times. Threetechniques were employed to control the STATCOM, namely, fuzzy logic,particle swarm optimization and a combination of both. A performancecomparison was made among the three soft computing techniques used tocontrol the STATCOM on the basis of the amount of voltage compensationoffered at the point of common coupling. The simulations were done with thehelp of SimPowerSystems available with MATLAB / SIMULINK and theresults validated that the STATCOM controlled by all the three techniques offered voltage compensation in all the cases considered.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
In this paper, we focus on the modeling and control of a wind power system based on a Permanent Magnet Synchronous Generator (PMSG). We proposed a technique of control strategies to have the maximum power from wind turbine (WT). This study deals with the problem of Maximum Power Point Tracking (MPPT) based on Takagi Sugeno fuzzy model. The stability analysis is achieved. The gains of the designed controller are calculated by solving Linear Matrix Inequality (LMI). Finally, simulation results are provided to demonstrate the validity and the effectiveness of the proposed method.
Similar to Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
2. TELKOMNIKA ISSN: 1693-6930
Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting (Jianhong Zhu)
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same time, it may achieve a fast power regulation to improve the stability of the wind power
system and the reliability of the power supply by a small storage capacity [13-18].
In this paper, a wind power schedule forecast error correction method is proposed by
means of a hybrid algorithm integrated with a physical technique. Firstly, back propagation (BP)
neural network in power pre-fabrication is proposed not only containing Mathematical statistics
(MS), but also considering artificial neural network integrated with particle swarm algorithm
(PSO-BP). Secondly, analysis and comparison of several intelligent algorithms (BP, RBP, PSO-
BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the proposed prediction
method. Finally, an improved wind power schedule forecast correction system based on storage
batteries is used to improve schedule forecast accuracy, optimal capacity of the storage battery
is studied, practical operating data are used in MATLAB simulation.
2. Power Prediction Technique Based on MS-PSO-BP
2.1. Mathematical statistics (MS) sample data pretreatment
In theory, a single wind turbine output power can be obtained by formula (1) [19], while
in actual wind farm, because of the influence of the external environment and different wind
turbines characteristics, error exists on the actual power curve distribution, as shown in
Figure 1.
32
2
1
wpm VRC•P (1)
Figure 1. Normal wind speed-power curve
Due to numerical weather prediction error, wind farm actual power output does not quite
coincide with the prediction. In neural network training and prediction process, numerical
weather prediction data are essential, so it is necessary to study the relationship between
numerical weather forecast and the actual power output.
In this paper, the sampling period used for numerical weather forecast is 10 min, mainly
including wind speed, wind direction and temperature data. According to the formula (1), the
wind speed is the most important factor that influences the turbine power output, so the
research is mainly on the relationship between wind speed and wind power. Figure 2 is shown
the relationship between wind farm power output and the forecast wind speed.
It is seen from which that relationship is not accord completely with the wind power
curve in Figure 1. To the same forecast wind speed,the actual power output may be different.
The training sample of neural network as such historical statistical data will influence the training
effect, lower convergence rate. For large amount data, mathematical statistics is an effective
analysis method. To analyze and predict the relationship more precisely between the actual
output power and wind speed of wind farm, probability statistics method is used. Wind speed
partition is done to realize convenient analysis, partition statistics is referred to IEC61400-12
standard. The wind speed data collected should cover range from -1m/s (cut into the wind
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speed) to 1.5 m/s multiplied by 85% of the rated wind speed. According to Bin methods [20],
wind speed range collected is adopted 2 m/s to 20 m/s, divided by 1 m/s,the center value of
each bin is the integer times of 1 m/s, each bin will contain a lot of wind speed scatters.
Figure 2. 100MW wind farm power output and wind speed forecast relations
By this means, the wind speed data from the numerical weather prediction are divided
into several partitions. In each BIN interval, wind farm actual power output are more dispersed.
To get a detailed analysis of each BIN range of power distribution, taking the 9.5 m/s ~ 10.5 m/s
wind speed range as an example, kernel density estimation algorithm is used to compute
density distribution characteristics of the power output [21], the calculation formula is shown in
formula (2), where f is probability density correspond to iP , P is power output point of BIN
range(9.5 m/s ~ 10.5 m/s). According to the formula (2), wind farm output power distribution can
be calculated among the wind speed range, as shown in Figure 3.
, i
f P KSDE P (2)
Figure 3. 9.5m / s ~ 10.5m / s wind speed- power output distribution of wind farm
Similarly, statistics for actual power output correspond to each BIN interval is done, it can
be seen from the Figure 3 and Figure 4, power distribution presents certain regularity in each
BIN interval.
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Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting (Jianhong Zhu)
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Figure 4. Power distribution of different BIN ranges confidence low limit
1Ndown Nup
P P P P (3)
At first, the power output probability distribution of each BIN interval is concentrated on
the peak probability density, and decreased symmetrically on both sides. Next, along with the
increased wind speed BIN ranges, probability distribution curve correspond to the peak also
increases gradually, moving to left. To revise small probability power output, bin estimation
theory is used to construct an estimated interval ,Ndown NupP P , making estimation range cover P
within the probability 1 . The calculation method is as shown in formula (3). NdownP and NupP is the
confidence low limit.
Upper limit of parameters P in the Nth interval, no longer labeled N in the following, downP
and upP represents same meaning as NdownP and NupP . Some BINs probability distribution in the
interval [0,100000] are not symmetrical completely in Figure 4, divided mainly into three
conditions in Figure 5, peak to the left (a), peak center (b), peak to the right (c).
Figure 5. BINs probability distribution (left/mid/right)
To guarantee larger probability of power points appeared in the estimates range,
considering the probability symmetry distribution on both sides of the peak, the formula (4) (5)
(6) are adopted to estimate intervals of above three conditions respectively.
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TELKOMNIKA Vol. 15, No. 4, December 2017 : 1610 – 1624
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max
0 0
1
( ) ( ) 1 , ( ) 1
2
upP P
F x f P dP f P dP (4)
max max
max 0
1 1 1
( ) ( ) ( ) 1 , 1 ( ) 1 1
2 2 2
up
down
P P P
P P
F x f P dP f P dP f P dP (5)
max100000
0
1
( ) ( ) 1 , ( ) 1 1
2down
P
P
F x f P dP f P dP (6)
When probability integral of left half of peak is less than 1
1 1
2
, the probability density
curve is as shown in Figure 5 (a), it can be seen 0downP , upP can be obtained by the formula (4),
distribution range is as upP,0 .When probability integral is less than 1
1 1
2
, but greater than
1
1
2
, the probability density curve is as shown in Figure 5 (b). According to the formula (5), downP
and upP can be obtained, the distribution range is updown PP , . When probability integral is greater
than 1
1 1
2
, the probability density curve is as shown in Figure 5 (c), 100000upP , downP can be
obtained by the formula (6). The distribution range is 100000,downP .Once the confidence level is
selected, the confidence lower limit and the confidence upper limit of each BIN can be obtained,
using these values as midpoints of each BIN interval, confidence lower limit curve downcurveP and
confidence upper limit curve upcurveP can be fitted by the interpolation algorithm, as shown in
Figure 6.
According to the above curves, neural network training data are revised, smaller
probability data points beyond confidence curve are amended as formula (7).Where
*
P is the
actual output power of wind farm, P is the corrected power output, which will be served as the
training samples of neural network.
*
* *
*
dowmcurve downcurve
downcurve upcurve
upcurve upcurve
P P P
P P P P P
P P P
(7)
Figure 6. Confidence upper and lower limit schematic
2.2. Revised BP Neural Network model
At present, the representative models of the neural network are BP (back
propagation) neural network and RBF (radial basis function) neural network [22,23]. They
have some advantages, such as strong robustness and fault tolerance, self-learning, self-
organization, adaptability, and can approach to arbitrary complex nonlinear relationship.
The paper used them in wind power prediction, and prediction effects are compared and
analyzed with each other.
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2.2.1. BP neural network model
The typical BP neural network shows three layers structure, including the input layer,
middle layer and output layer. Middle layer can be designed as single hidden layer or multi-
hidden layers structure. The core of the algorithm is forward information dissemination and error
back propagation, the process is done again and again, and the weights of each layer and
threshold is adjusted continuously, finally the error is reduced to an acceptable level. Assume
number of input layer nodes is n, the middle layer number p, output layer m, so : n m
f R R is
completed. The input and output topology structure is as shown in Figure 7.
……
…………
n mq
……
WV
j
1X
iX
nX
1z
kz
pz
1y
jy
my
Figure 7. Three-layer neural network topology of BP
The output of the node j of middle layer is as formula (8).Where i along to [1 ,n], j along
to [1 ,p], the output of the node k is as formula (9).
n
u f w x i n j p
j i j i j
i
),( 1 ,1
1
1
(8)
2
1
,1
p
k j k j k
j
y f v Z K m
(9)
Among formula (8)(9), the k [1 ,m], 1
f is the transfer function of hidden layer, 2
f is the
transfer function of output layer, i
x represents each neuron input of the input layer, i j
w is the
weight of the input layer to the middle layer, j
is the middle layer node threshold, j k
v is the
connection weight from the middle layer to output layer, k
is the output layer thresholds, the
initializations of weights and thresholds are produced by random, and the random initial value
tends to reduce the convergence speed, easy to make the training results fall into local
minimum value.
2.2.2. RBF neural network model
The Radial Basis Function Neural Network (Radial Basis Function Neural Network, the
RBFNN) is a kind of feed forward Neural Networks. Compared with the BP neural network, RBF
neural network not only has a physiological basis, but a simple structure, concise training and
fast convergent speed. RBF neural network also has the three layers structure. The weight
between input layer and hidden layer is fixed to 1, only the weight between hidden layer and
output layer is adjustable. Number of input layer nodes is n, middle layer p, output layer m, so
: n m
f R R .The input and output model is as shown in Figure 8.The output of the j hidden node
is as formula (10).
j j jh X c (10)
1
m,1
h
j i j i j
i
y w X c j
(11)
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…
Input layer Output layerHidden layer
…
1 1,x c
2 2,x c
,k kx c
1x
2x
px
1iw
2iw
kiw
if x
Figure 8. RBF neural network structure
In the formula (11), 1 2, nX x x x is the input vector, 1 2,j j j j nc c c c is the first j
hidden node of RBF data center. j
is the activation function of the hidden nodes, generally,
the Gaussian function is taken as
2
2
u
u e
.It can be seen that the key to establish a RBF
network model is determining the number of hidden layer of RBF network h and data center j
c ,
the width of the radial basis functionσ,the connection weight from output neurons to the hidden
layer neurons is i j
w .Like BP neural network, the RBF neural network, for key parameters,
initialization is generated randomly, these random initialization values affect the neural network
training in a certain extent.
2.2.3. Improved Neural Network under adaptive mutation Particle Swarm Optimization
(PSO)
RBF neural network and BP neural network are used widely, also having some
deficiencies, such as long training time, easy falling into local minimum value, and so on.
Although RBF neural network is better due to its convergence speed and local minimum value
problem, but parameter initialization value is generated randomly, so the neural network training
is affected in a certain extent. In order to be able to solve these problems, the PSO algorithm is
introduced to improve neural network algorithm.
PSO basic idea is inspired by the birds swarm behavior regularity, a simplified model of
swarm intelligence is then established. It is an optimization algorithm based on iterative process.
The first is to initialize a group of particles, each particle has two characteristics, position and
velocity. The position of each particle is representative for a possible solution of optimization
problem, and the velocity of the particle is expressed on the direction and distance of flight.
Then optimal particle in the solution space is searched through iteration. For each iteration, the
particle individual position is updated by tracking individual extremum best
P and group extremum
best
G , until the optimal particle is found. Setting a group consisted of m particles and fly at a
certain speed in D dimensional search space, then the particle swarm can be expressed on
1 2, , DX X X X L , i
X represents the ith particle's position, also represents a possible solution of
problem, it can be expressed with matrices 1 2
, ,
T
i i i i D
X X X X L .The fitness value of the each
position of particle can be calculated by the substitution of i
X into the objective function. best
P
represents the ith particle speed, it can be expressed on 1 2
, ,
T
i i i i D
V V V V L . Particles update
speed and position according to formula (12) (13).
1 1 2 2
1i j i j j i j j i j
V t V t C R P t X t C R G t X t (12)
1 1i j i j i jX t X t V t (13)
Among formula (12) (13), j = 1, 2,..., d, represents particle dimension, t is the times of
iteration, 1
C and 2
C are learning factors, 1
R and 2
R represent random number between 0~1, as
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inertia weight, i j
V t represents the ith particle current speed in the tth generation, i jX t
represents the ith particle current location in the tth generation. In the general PSO algorithm,
inertia weight represents the impact of the historical rate on the current speed. Where the larger
is, the stronger global search ability particles have. While the lower is, the stronger portion
search ability particles have. Once 0 , it means that the particles lose ‘memory’. To give
attention to both global and local search, take as linear gradient, in formula (14), i t er is
current iteration number, max
i t er is the maximum number of iterations, max
is inertia weight initial
value, mi n
represents inertia weight ultimate value. To prevent particles from blind search, the
particle's position and speed are limited respectively within a certain range[ ]max max
X ,X and
[ ]max max
V ,V .
max max mi n
max
i t er
i t er
(14)
In order to avoid the ‘precocity’ and low iterative efficiency of PSO algorithm, the
mutation is introduced to PSO algorithm, the principle is that the population is initialized at
certain probability after each updating, so expanding search space of the dwindling population
during the process of iteration, ensure the optimal location be searched before jumping out, thus
improving the global convergence of the algorithm.
2.2.4. BP Neural Network combined with PSO on prediction model
The improved PSO algorithm is used to optimize parameters of neural network
prediction model [24]. The steps are as following.
Step 1 Building particle swarm, a neural network topology structure is established
according to the input and output sample. The parameters be to optimize are coded to the
individual particles of real vector population.
Step 2 The initialization of particle swarm parameters, mainly including the size of the
population, learning factor, particle position and velocity interval, number of iterations, etc.
Step 3 Calculating the particle fitness value, according to the input and output sample,
the fitness function value of each particle is calculated, the current position is set to itself optimal
location, the position of optimal particle in initialization population is set to the global optimal
position.
Step 4 Loop iteration, PSO algorithm formula (12) (13) are used to update particle
velocity and position.
3. Wind/Storage Dynamic Correction of Schedule Forecast
It can be seen from literatures that prediction error exists inevitably in theoretical
prediction algorithms due to various complex factors. The storage system can be used to
absorb the redundant energy, correct unforeseen owed power supply, lower prediction errors
and improve the accuracy of wind power forecast due to flexible charge-discharge
characteristic. In Figure 9, the wind/storage system is mainly composed of wind turbines and
vanadium battery package. The power relationship is as shown in formula (15). Where, a
P is the
actual power output of the wind farm, b
P is charging and discharging power of battery energy
storage system, d
P is the whole Wind/Storage system output. The key is how to determine b
P
dynamically. From the foregoing description, MS-PSO-BP neural networks has been used to
give modified predicted power '
)(P
P t firstly, once the actual power output is greater than the
predicted wind farm output ( ( )a p
P P t ), then the energy storage system is charged to guarantee
the accuracy of the forecast wind power ( ( )d p
P P t ), and b
P is set to a negative value. On the
contrary, when ( )a p
P P t , b
P is set to a positive value, the energy storage system is discharged.
When ( )a p
P P t and '
( )d P
P P t , b
P is set zero, the battery energy storage system is kept in
holding state. e
p is the prediction error tolerance. ( )error
p t is the error between modified
prediction power and real-time power. Algorithm flowchart is as shown in Figure 10. If the
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modified prediction power value from MS-PSO-BP neural network lies in the allowed range
error, the predictive value is regarded as optimal forecasting power of the moment. On the
contrary, if the predictive value is out of the range, then the energy storage system is triggered
to amend the predicted value.
Gearbox
Gearbox
...
AC/DC
..
Pa
Pb
Pd
Wind Turbines
Energy
storage
system
Grid
Figure 9. Configuration of wind farm combined with storage battery
d b a
P P P (15)
Figure 10. Wind/storage amending algorithm flowchart
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4. Simulation and Analysis
4.1. BP/RBP/PSO-BP prediction without training sample pretreatment
In order to verify the effectiveness of the proposed method, a real wind farm data
samples are used to test the validity of the algorithm, the wind farm is located in the coastal
areas of Jiangsu, and installed capacity is 100 MW. Full-year data of 2014 are used in
mathematical statistics, the first two months date of 2015 are used as training sample, the data
of March are used in test, data sampling period is 10 min, mainly including wind speed, wind
direction, temperature and wind power data, all mathematical operation are performed by
normalized process. To distinguish the pros and cons of the different algorithms, root mean
square error (RMSE)and mean absolute error(MAE) are used to measure wind power prediction
error, and the calculation formula is as (16) (17).
2
1
n
Mi Pi
i
P P
RMSE
C n
g
(16)
1
n
Mi Pi
i
P P
MAE
C n
g
(17)
MiP is the real power of the moment i, PiP is the prediction power of the moment i, C is
power capacity of the field wind farm, n is as the number of sample. Taking wind power on
March 1, 2015 as prediction object, using BP neural network, PSO-BP neural network, RBF
neural network respectively to predict 96 wind power points from 0:00 to 24:00. Prediction
results are shown in Figure 11. The RMSE and MAE results are as shown in Table 1. It can be
seen that prediction results of three neural network algorithms are improved compared with
existing wind farm forecast accuracy.
Figure 11. Wind power prediction results of BP/RBP/PSO-BP/actual field
Table 1. Error Statistics
Algorithm RMSE (%) MAE(%)
Existing wind farm prediction 15.09 12.12
RBF 14.37 12.18
BP 14.57 12.21
PSO-BP 13.52 11.77
BP neural network prediction results are similar to RBF neural network, but the
prediction accuracy of BP neural network algorithm is improved obviously through amendment
of PSO algorithm. RMSE is reduced by 10.40%, and MAE is reduced by 2.89% compared with
existing prediction. Considering uncertainty of the neural network algorithm, the above methods
are tested repeatedly, prediction results are analyzed. The wind farm power output are predicted
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from March 1 to March 20, and the RMSE and MAE results are as shown in Appendix Table 1
and Appendix Table 2. It can be seen that RBF neural network, BP neural network and PSO-BP
neural network prediction results are uncertain for single prediction, so the average prediction
error are analyzed overall, average prediction error of the 20 days ago on the March are as
shown in Figure 12. Prediction error of RBF neural network prediction system is similar to
existing wind power prediction. BP neural network overall training effect is better than RBF
neural. Its average RMSE is reduced by 8.59% compared with RBF neural network and reduced
by 7.13% compared with existing prediction system of wind farm. Furthermore, PSO-BP neural
network is 2.7% lower than the average RMSE, the same for the MAE, reduced by 8.44%
compared with the existing wind power prediction system, reduced by 2.83% and 2.83%
respectively compared with the BP and RBF prediction algorithms.
Figure 12. Comparison chart of the average prediction error on wind power
4.2. BP/RBP/PSO-BP prediction with training sample pretreatment
To verify effectiveness of training data pretreatment for prediction precision and
convergence speed for the neural network algorithm, the proposed mathematical statistics
method is used to modify neural network training samples. Selecting confidence level
95.01 , according to formula (4) (5) (6), the corresponding confidence lower limit curve
downcurveP and confidence upper limit curve upcurveP are obtained. Using the formula (7) to correct
training data, the diagram is shown in Fig 13. After correction, data points of small probability
have been processed, preventing the "mixed" from affecting on neural network training. The
results of average RMSE and MAE are shown in Table 2.
Figur 13. Data revised schematic diagram
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Table 2. Prediction Error Comparison Before and After the Training Data Pretreatment
Algorithm RMSE(%)
RMSE(%)
Date revised
MAE(%)
MAE(%)
Date revised
Existing wind farm
prediction
16.40 12.79
RBF 16.65 16.17 12.91 12.85
BP 15.23 14.80 12.05 11.70
PSO-BP 14.83 13.89 11.71 11.54
Data show that prediction errors of three algorithms after sample pretreatment are
decline in various degrees, RMSE of RBF, BP, PSO-BP reduced by 2.90%, 2.82% and 6.34%
respectively, and the MAE just reduced by 0.4%, 2.90% and 0.4% respectively, it can be seen
that the accuracy after pretreatment is improved effectively. Another advantage is the training
speed, some "special points" are prevented from slow convergence or even not convergence.
Figure 14 and Figure 15 show training effects of the BP neural network and RBF neural network
under pretreatment or no. It can be seen from Figure 14, under the condition of the same
training target, training with data pretreatment has better training effect on training speed and
precision compared with that untreated. RMSE reaches steady state (0.019) only after 28 times.
Figure 14. BP neural network training with and
without data pretreatment
Figure 15. RBF neural network training with
and without data pretreatment
While without data pretreatment, it needs 46 times training and RMSE reaches the steady
state (0.024). It can also be seen from the Figure 15, under the condition of the same training
target, RBF neural network training after data pretreatment need only 23 times to reach the
target value of 0.01. Without pretreatment, no training goals can be arrived even after a
maximum of 200 times. Comparing Figure 14 with Figure 15, it can also be seen that algorithms
convergence speed and convergence results of BP are all inferior to that of RBF, showing
feasibility and effectiveness of the experimental results.
4.3. Wind/storage system physical amending for schedule forecast
The simulation experiment is done in a wind farm of 100000 kilowatts in eastern coastal
of China. The historical statistical data are concluded within 4 consecutive days in June, such as
wind speed, the wind field actual output power. The data during previous two days are adopted
as training samples for prediction model. The measured data in the next two days are regarded
as the calibration data, which are used to verify the accuracy and yet the validity of the
algorithm.
The results of simulation are shown in Figure 16. Prediction errors simulation are as the
basis to determine the battery capacity needed. In order to prevent the prediction process from
the occasional error, experiments are conducted 1000 times, each maximum capacity max
C of
battery charge or discharge is stated, and a high level of confidence max
C is selected for
referenced battery capacity. When certain capacity is supplied in the wind power system, the
5 10 15 20 25 30 35 40 45 50 55 60
0
0.02
0.04
0.06
0.08
0.1
0.12
Epochs
MeanSquaredError(mse)
BP
Processed BP
Goal
0 20 40 60 80 100 120 140 160 180 200
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055
Epochs
MeanSquaaredError(mse)
RBF
Processed RBF
Goal
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results of reducing the prediction errors are as shown in Figure 17 and Figure 18. As can be
seen from Figure 17, the power output Pd of the wind farm is smooth more with battery energy
storage system, and is close to the prediction curve of wind power. Figure 18 shows that the
battery capacity can also meet the needs of short-term wind power correction. Meanwhile, it is
concluded from the experiments that RMSE of wind power forecast has been reduced to
9.3535e+003W, 10 minutes predictive error integration is reduced to 2.1941e+004Ah, the
battery energy storage system can minify wind power prediction error effectively.
Figure 16. Modified Power prediction error integration
Figure 17. Power output forecast correction
with battery energy storage
Figure 18. Storage battery SOC change during
correction
5. Conclusion
This paper combines the mathematical statistics and BP neural network on wind power
prediction, PSO algorithm is used to improve prediction precision. Based on which,
Wind/Storage system is used to amend wind farm power forecast. Simulation results show that
the proposed pretreatment (mathematical statistics method) can improve the neural network
training speed and precision. In addition, the PSO algorithm can also improve the prediction
precision of the BP neural network effectively. Compared with the current wind farm forecasting
strategy, RMSE of PSO-BP can be reduced by 6.34%, and the MAE reduced by 0.4%.
Moreover, the schedule forecast accuracy can be improved effectively by physical Wind/Storage
dynamical correction, and experiments show that RMSE of wind power forecast has been
reduced to 9.3535e+003W, the energy storage system can minify wind power prediction error
effectively. However, limited by the technical conditions, the battery capacity is still an important
bottleneck in application all along. The battery capacity can be insufficient for big share on the
access of large wind farms, but it can be used as a power fine-tuning in large wind power.
0 20 40 60 80 100 120 140 160 180 200
-150
-100
-50
0
50
100
150
200
Time(T=15min)
Capacity(MAh)
0 50 100 150
0
20
40
60
80
100
Time(T=15min)
P(MW)
Forecast corrected with battery
Forecast corrected without battery
0 50 100 150 200
0
0.2
0.4
0.6
0.8
1
Time(T=15min)
SOC
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Acknowledgements
This work is a part of the Natural Science Foundation of China (No.
51377047,No.51407097). Experimental data are taken from Jiangsu Longyuan Wind Power
Co., Ltd..The authors would like to thank for the supports from both the Ministry of Science and
Technology of China and National Natural Science Foundation of China and Jiangsu Longyuan
Wind Power Co., Ltd. of China.
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Appendix
Appendix Table 1 . RMSE Contrast of Algorithm Proposed on Paper with on-Site Original
Forecast
Date
Wind Forecasting
System
Training data uncorrected Training data corrected
RBF BP PSO-BP RBF BP PSO-BP
3.1 15.09 14.37 14.56 13.51 15.46 14.41 14.31
3.2 11.54 11.73 12.37 9.99 12.44 12.65 9.95
3.3 24.69 25.32 21.63 20.14 23.18 20.27 20.96
3.4 24.15 20.36 24.39 23.25 21.60 23.71 21.18
3.5 10.51 15.30 13.26 18.82 12.36 12.30 12.37
3.6 8.21 12.44 10.00 11.32 6.22 11.61 9.69
3.7 7.74 10.88 8.74 10.76 6.96 8.11 9.03
3.8 33.46 33.20 33.24 32.77 33.73 33.76 30.99
3.9 21.99 27.73 14.90 12.82 21.86 13.38 13.72
3.1 36.35 28.47 22.44 19.33 24.02 20.04 22.01
3.11 4.63 6.93 5.20 3.74 5.79 6.48 5.12
3.12 14.5 14.44 12.14 12.56 11.70 1045 10.05
3.13 12.17 12.06 13.27 12.88 25.63 13.44 10.54
3.14 12.16 12.03 14.35 9.39 12.24 13.11 11.20
3.15 11.7 14.89 12.67 13.40 13.35 11.51 12.63
3.16 10.22 10.30 10.31 9.03 10.04 9.04 9.02
3.17 13.72 14.72 12.60 15.73 11.44 13.33 12.42
3.18 33.56 28.01 25.24 28.21 29.42 27.59 21.87
3.19 13.3 12.23 12.91 10.74 11.93 11.70 12.60
3.20 8.21 7.64 10.25 8.13 13.94 9.08 8.13
Mean 16.395 16.65 15.23 14.83 16.17 14.80 13.89
Appendix Table 2. MAE Contrast of Algorithm Proposed on Paper with on-Site Original Forecast
Date
Wind Forecasting
System
Training data uncorrected Training data corrected
RBF BP PSO-BP RBF BP PSO-BP
3.1 12.12 12.18 11.59 11.46 12.18 12.14 12.42
3.2 8.94 9.24 9.37 10.05 9.06 8.67 10.25
3.3 18.56 22.61 16.39 16.67 22.72 15.83 17.47
3.4 16.81 16.28 22.35 20.71 16.19 18.75 19.47
3.5 8.22 12.74 10.84 11.22 12.24 11.20 8.52
3.6 7.07 9.98 7.94 8.81 9.49 8.79 8.98
3.7 5.41 6.11 7.59 6.06 5.89 6.12 2.35
3.8 26.20 26.68 26.53 27.36 26.88 24.81 26.78
3.9 18.80 17.93 11.22 9.82 18.53 11.11 10.55
3.1 25.01 19.92 9.85 14.44 20.07 15.73 15.47
3.11 3.37 5.71 5.20 3.71 4.66 3.96 4.84
3.12 11.44 13.04 9.01 10.74 12.85 7.63 8.35
3.13 9.91 8.15 14.25 9.12 9.00 10.06 9.92
3.14 9.71 8.35 9.95 9.37 8.32 11.09 11.16
3.15 10.05 12.16 12.84 9.95 12.00 11.36 9.54
3.16 8.23 7.83 7.32 6.85 7.76 6.62 7.17
3.17 11.67 12.45 9.80 9.44 12.41 9.69 10.83
3.18 27.87 23.01 23.18 21.52 22.99 23.79 19.09
3.19 10.66 8.80 9.30 10.45 8.77 10.05 8.90
3.2 5.68 5.04 6.51 6.47 5.08 6.66 8.82
Mean 12.79 12.91 12.05 11.71 12.85 11.70 11.54