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.
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.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
Optimal tuning of a wind plant energy production based on improved grey wolf ...journalBEEI
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
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.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
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.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
Optimal tuning of a wind plant energy production based on improved grey wolf ...journalBEEI
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
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.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
Solar irradiance uncertainty management based on Monte Carlo-beta probability...journalBEEI
In recent years, solar PV power generation has seen a rapid growth due to environmental benefits and zero fuel costs. In Malaysia, due to its location near the equator, makes solar energy the most utilized renewable energy resources. Unlike conventional power generation, solar energy is considered as uncertain generation sources which will cause unstable energy supplied. The uncertainty of solar resource needs to be managed for the planning of the PV system to produce its maximum power. The statistical method is the most prominent to manage and model the solar irradiance uncertainty patterns. Based on one-minute time interval meteorological data taken in Pekan, Pahang, West Malaysia, the Monte Carlo-Beta probability density function (Beta PDF) is performed to model continuous random variable of solar irradiance. The uncertainty studies are needed to optimally plan the photovoltaic system for the development of solar PV technologies in generating electricity and enhance the utilization of renewable energy; especially in tropical climate region.
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
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.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
This paper presents the maximum power point tracking (MPPT) to extract the power of wind energy conversion system (WECS) using the Firefly Algorithm (FA) algorithm. This paper aims to present the FA as one of the accurate algorithms in MPPT techniques. Recently, researchers tend to apply the MPPT digital technique with the P n O algorithm to track MPP. On the other hand, this Paper implements the FA included in the digital classification to improve the performance of the MPPT technique. Therefore, the FA tracking results are verified with P n O to show the accuracy of the MPPT algorithm. The results obtained show that performance is higher when using the FA algorithm.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Robust Evolutionary Approach to Mitigate Low Frequency Oscillation in a Multi...IDES Editor
This paper proposes a new optimization algorithm
known as Modified Shuffled Frog Leaping Algorithm (MSFLA)
for optimal designing of PSSs controller. The design problem
of the proposed controller is formulated as an optimization
problem and MSFLA is employed to search for optimal
controller parameters. An eigenvalue based objective function
reflecting the combination of damping factor and damping
ratio is optimized for different operating conditions. The
proposed approach is applied to optimal design of multimachine
power system stabilizers. Three different power
systems, A Single Machine Infinite Bus (SMIB), four-machine
of Kundur and ten-machine New England systems are
considered. The obtained results are evaluated and compared
with other results obtained by Genetic Algorithm (GA).
Eigenvalue analysis and nonlinear system simulations assure
the effectiveness and robustness of the proposed controller in
providing good damping characteristic to system oscillations
and enhancing the system dynamic stability under different
operating conditions and disturbances.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Wind Turbine Power Generation: Response PredictionIOSR Journals
The worldwide interest has been increasing about wind energy for power generation purpose due to
continue increase in fuel cost and the need to have clean source of energy. Wind energy may enhance the power
generation capabilities and maximize its capacity factor, inurn participate in generating power at lower cost. It
has also been notice that renewable power generation through wind energy is also the fast growing energy
technology. The optimization of the efficiency of wind turbine is prudent to complete the conventional power
sources. Wind Turbine Power Generation (WTPG) is a complex phenomenon to understand since the real
process has depends upon the Wind Velocity and the relative turbine dimensions and the outside climatological
parameter like Wind Velocity (WV) nature of wind, etc. In this paper an effort has been made to develop a fuzzy
logic approach to predict an appropriate WTPG considering the WV, AD and Chord Length of Turbine Blades
(CLTB) as input parameters. The complexities of the parameters and the imprecision of linguistic expressions
are taken into consideration; the application combining linguistic variable to optimize WTPG under multiple
conditions is presented in this study.
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%.
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.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasti...IJECEIAES
The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ( [ 1,2,7,16,18,35,46 ] , 1, [ 1,3,13,21,27,46 ] )( 1,1,1 ) 48 ( 0,0,1 ) 336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
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.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
Solar irradiance uncertainty management based on Monte Carlo-beta probability...journalBEEI
In recent years, solar PV power generation has seen a rapid growth due to environmental benefits and zero fuel costs. In Malaysia, due to its location near the equator, makes solar energy the most utilized renewable energy resources. Unlike conventional power generation, solar energy is considered as uncertain generation sources which will cause unstable energy supplied. The uncertainty of solar resource needs to be managed for the planning of the PV system to produce its maximum power. The statistical method is the most prominent to manage and model the solar irradiance uncertainty patterns. Based on one-minute time interval meteorological data taken in Pekan, Pahang, West Malaysia, the Monte Carlo-Beta probability density function (Beta PDF) is performed to model continuous random variable of solar irradiance. The uncertainty studies are needed to optimally plan the photovoltaic system for the development of solar PV technologies in generating electricity and enhance the utilization of renewable energy; especially in tropical climate region.
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
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.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
This paper presents the maximum power point tracking (MPPT) to extract the power of wind energy conversion system (WECS) using the Firefly Algorithm (FA) algorithm. This paper aims to present the FA as one of the accurate algorithms in MPPT techniques. Recently, researchers tend to apply the MPPT digital technique with the P n O algorithm to track MPP. On the other hand, this Paper implements the FA included in the digital classification to improve the performance of the MPPT technique. Therefore, the FA tracking results are verified with P n O to show the accuracy of the MPPT algorithm. The results obtained show that performance is higher when using the FA algorithm.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Robust Evolutionary Approach to Mitigate Low Frequency Oscillation in a Multi...IDES Editor
This paper proposes a new optimization algorithm
known as Modified Shuffled Frog Leaping Algorithm (MSFLA)
for optimal designing of PSSs controller. The design problem
of the proposed controller is formulated as an optimization
problem and MSFLA is employed to search for optimal
controller parameters. An eigenvalue based objective function
reflecting the combination of damping factor and damping
ratio is optimized for different operating conditions. The
proposed approach is applied to optimal design of multimachine
power system stabilizers. Three different power
systems, A Single Machine Infinite Bus (SMIB), four-machine
of Kundur and ten-machine New England systems are
considered. The obtained results are evaluated and compared
with other results obtained by Genetic Algorithm (GA).
Eigenvalue analysis and nonlinear system simulations assure
the effectiveness and robustness of the proposed controller in
providing good damping characteristic to system oscillations
and enhancing the system dynamic stability under different
operating conditions and disturbances.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Wind Turbine Power Generation: Response PredictionIOSR Journals
The worldwide interest has been increasing about wind energy for power generation purpose due to
continue increase in fuel cost and the need to have clean source of energy. Wind energy may enhance the power
generation capabilities and maximize its capacity factor, inurn participate in generating power at lower cost. It
has also been notice that renewable power generation through wind energy is also the fast growing energy
technology. The optimization of the efficiency of wind turbine is prudent to complete the conventional power
sources. Wind Turbine Power Generation (WTPG) is a complex phenomenon to understand since the real
process has depends upon the Wind Velocity and the relative turbine dimensions and the outside climatological
parameter like Wind Velocity (WV) nature of wind, etc. In this paper an effort has been made to develop a fuzzy
logic approach to predict an appropriate WTPG considering the WV, AD and Chord Length of Turbine Blades
(CLTB) as input parameters. The complexities of the parameters and the imprecision of linguistic expressions
are taken into consideration; the application combining linguistic variable to optimize WTPG under multiple
conditions is presented in this study.
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%.
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.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasti...IJECEIAES
The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ( [ 1,2,7,16,18,35,46 ] , 1, [ 1,3,13,21,27,46 ] )( 1,1,1 ) 48 ( 0,0,1 ) 336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
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.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
Embedded Applications of MS-PSO-BP on Wind/Storage Power ForecastingTELKOMNIKA JOURNAL
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.
Performance of low-cost solar radiation loggerIJECEIAES
In solar power systems, irradiance value data are among the most important parameters. Such data can be used in installing photovoltaic (PV) modules, such as determining the exact location, tilt angle, and required area, for optimal power efficiency. In this study, the comprehensive simulation and implementation of a solar radiation meter with a PV cell and temperature sensor are presented. The irradiance measurement value is based on the power reading generated by the small capacity of the PV cell at a specific load converted into a digital value in the microcontroller using the implicit Newton polynomial interpolation (NPI) equation as a low-cost alternative method. The effect of temperature is included in the conversion to obtain precise measurement results. Firstly, the structure and characteristics of the PV cell are discussed. Secondly, the parameters, measuring method, and conversion of the measurement reading data using the NPI equation are presented to assess the results. Finally, the simulation of the solar radiation meter using the PSIM and implementation of the hardware are conducted to validate the concepts and compare their results. The proposed hardware has an average error of 2.72% in the implementation of the measurement test.
Adaptive maximum power point tracking using neural networks for a photovoltai...Mellah Hacene
Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid
Electrical Engineering & Electromechanics, (5), 57–66, 2021. https://doi.org/10.20998/2074-272X.2021.5.08
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Short Term Load Forecasting: One Week (With & Without Weekend) Using Artifici...IJLT EMAS
This paper present for analysis of short term load forecasting: one week (with & without weekend) using ANN techniques for SLDC of Gujarat. In this paper short term electric load forecasting using neural network; based on historical load demand, The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB.12 ANN tool box. Design a model for one week (with & w/o weekend) load pattern for STLF using the neural network have been input variables are (Min., Avg., & Max. load demands for previous week, Min., Avg., & Max. temperature for previous week & Min., Avg., & Max. humidity for previous week). And Nov-12 to Apr-13 (6 Months) historical load data from the SLDC, Gujarat are used for training, testing and showing the good performance. Using this ANN model computing the mean absolute error between the exact and predicted values, we were able to obtain an absolute mean error within specified limit and regression value close to one. This represents a high degree of accuracy.
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.
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.
This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power. The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.
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.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.
Data quality processing for photovoltaic system measurementsIJECEIAES
The operation and maintenance activities in photovoltaic systems use meteo- rological and electrical measurements that must be reliable to check system performance. The International Electrotechnical Commission (IEC) standards have established general criteria to filter erroneous information; however, there is no standardized process for the evaluation of measurements. In the present work we developed 3 procedures to detect and correct measurements of a pho- tovoltaic system based on the single diode model. The performance evaluation of each criterion was tested with 6 groups of experimental measurements from a 3 kWp installation. Based on the error of the 3 procedures performed, the most unfavorable case has been prioritized. Then, the reduction of errors between the estimated and measured value has been achieved, reducing the number of measurements to be corrected. For the clear sky categories, the coefficient of determination is 0.9975 and 0.9961 for the high irradiance profile. Although an increase of 2.5% for coefficient of determination has been achieved, the overcast sky categories should be analyzed in more detail. Finally, the different causes of measurement error should be analyzed, associated with calibration errors and sensor quality.
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.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3957 - 3966
3958
2. DESCRIPTION OF OBJECT
The studies concern a wind power plant located in the south-east Poland on hilly land. Its
topography creates very good wind conditions. In the east-west direction Figure 1 and to the north Figure 2,
within a radius of 10km there are no elevations that would exceed the terrain where the wind farm is located.
However, the only elevated area is located in the south direction within a radius of around 9.5 km with
the hills higher of about 150m Figure 2. Medium wind speed measured in the place where the turbines are
located is 7 m/s, that is why this terrain is the most suitable place locaet to a wind farm [14, 15].
The power plant comprises 5 turbines that are installed on 100-metre masts which are located as
shown in the scheme in Figure 3. The mast bases are situated at the level between 372 and 409 m.a.s.l.
with the distance from 290 to 650 m between one another Figure 3. Mounted wind turbines REpower MM92
with rated power of 2.05MW and rated voltage of 690V for the frequency equal to 50Hz. To set the wheel
of 92.5 diameter in motion, the wind needs to blow at about 3m/s. The running of the turbine stops when
the wind blows at 24 m/s and is reactivated at 22 m/s. In Figure 4 the turbine operates at low and medium
wind speed.
Figure. 1. Farm location in the south-north direction Figure 2. Farm location in the west-east direction
Figure. 3. Distribution of wind turbines
in the area
Figure 4. The characteristics of wind turbine
REpower MM92
3. FORECASTING METHODS
The paper presents short-term forecasting results conducted with the conservative method and using
artificial neural networks. In order to forecast with this method, it is sufficient to know the historical amount
of energy generation. The nature of neural networks allows to select input data (explanations) for predictions
from any area. However, to obtain a satisfactory effect, the forecast amount (energy generation) must depend
on the explanatory values, thus the selection of proper data is crucial.
3.1. Conservative (naive) method
The conservative model is the simplest way of predicting wind generation. It demands to generate
the power of wind generation in the moment i (of forecast generation) [16]:
𝑃𝑖+𝑘 = 𝑃𝑖 (1)
where:
𝑃𝑖+𝑘 : forecast power
𝑃𝑖 : value of power in the moment of forecasting
3. Int J Elec & Comp Eng ISSN: 2088-8708
The quality of data and the accuracy of energy generation forecast by artificial … (Bogdan Kwiatkowski)
3959
Such correlation, despite its simplicity, works very well when preparing forecasts prepared in
ultra-short (hourly) term. It is caused by a relatively conservative wind. In addition, the wind farm which
comprises a substantial number of wind stations occupies a substantial area, which is the reason why
individual units do not react at the same time to the change of wind speed. The forecasts are usually
conducted for 𝑘 = 1 and the method has the form of naïve method.
3.2. Neural networks
Artificial Neural Networks (ANN), both in the structure and operation, follow the nervous system of
living creatures. In order to use them for forecasts, it is necessary to conduct the training process of artificial
neural networks, that can be controlled or uncontrolled. The forecasts in the paper used feedforward neural
networks trained with the controlled methods. Then, the training set can be described with dependence:
𝑈 = {(𝒙𝑖 = 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛(𝑜𝑖), 𝒚 𝑗 = 𝑓(𝑜𝑖))}
𝑖=1
𝑁
(2)
where:
U : training set,
N : number of objects in training set,
𝑜𝑖 : i-th object of the training set (description of the object),
𝑥𝑖 : features’ vector of i-th object (input vector for ANN),
𝑦𝑗 : vector of ANN’s answers,
𝑓 : function of qualifications.
In case of controlled training methods, the purpose of a learner is to find weights’ matrix that allow
the conversion of input signals which describe the object (𝑥𝑖), for expected answers (𝑦𝑗). Thus, according to
the formula (2) the training set U comprises N of representing pairs: network input (describing the object’s
features) and corresponding answer. For not trivial cases it is usually impossible to find separating function 𝑓
which classifies objects in a proper way. That is why, the training process allows some error tolerance, so
that the learning process brings best results [17, 18].
The process of training artificial neural network is stochastic so, to some extent, it is unpredictable.
This weakness is at the same time SSN’s greatest strength-the feature saying that the network can solve
problems that people are unable to solve or we cannot describe using classic mathematical apparatus.
The whole knowledge of neural network is stored in the weights of particular neurons, and the training
process itself is about the modification of the weights values according to a set algorithm [19, 20].
Taking into consideration the kind of algorithm (of sets), according to which synaptic weights
modification is performed in the training process, a number of training rules can be distinguished. They treat
about the dependences according to which the neuron’s weight values are modified. Some of the rules are
related to the training mode with teacher, and others to training without teacher [18, 21]. Neuron output value
can be written with dependence:
𝑦 = 𝑓(∑ 𝑤𝑖 𝑥𝑖
𝑛
𝑖=0 ) = 𝑓(𝒘 𝑇
𝒙) (3)
where:
𝒙 = [−1, 𝑥1, 𝑥2 … 𝑥 𝑛] 𝑇
= [𝑥0, 𝑥1, 𝑥2 … 𝑥 𝑛] 𝑇
- vector of input signals;
𝑤 = [𝑏, 𝑤1, 𝑤2 … 𝑤 𝑛] 𝑇
= [𝑤0, 𝑤1, 𝑤2 … 𝑤 𝑛] 𝑇
- vector of synaptic weights;
U - neuron diaphragm potential.
The formula analysis (3) leads to a conclusion that an artificial neuron realizes the function of two
vector variables which convert the signal from n-dimensional space into one-dimensional space [22].
The transition function 𝑓 determines neuron’s “behaviour”. Most commonly used functions are: hyperbolic
tangent, hyperbolic sine, and linear function. Other types of functions are more rarely used.
4. IMPACT OF WEATHER CONDITIONS ON WIND FARM OPERATION
Power generated by a wind farm is a total power of its generators.
𝑃 = ∑ 𝑃𝑖
𝑛
𝑖=1 (4)
Power of an individual turbine (𝑃𝑖) is directly dependant on the wind strength:
𝑃𝑖 = 𝜂𝑖(𝑡)𝑃 𝑤𝑖𝑛𝑑 (5)
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3957 - 3966
3960
where:
𝑃 𝑤𝑖𝑛𝑑 : wind strength [W]
𝜂𝑖 :efficiency of i-th turbine
Wind strength can be described with dependence [15]:
𝑃 𝑤𝑖𝑛𝑑 =
1
2
𝜌𝐴𝑉3
(6)
where:
𝜌 : air density [kg/m2
],
A : area backed-off by the wheel (cross-section of air stream),
V : wind speed [m/s]
Taking into consideration that air density depends on its temperature, pressure and humidity:
𝜌 =
𝑝𝑀
𝑅𝑇
(7)
here:
𝑝 : pressure [Pa],
M : effective air molar mass [kg/mol],
R : gas constant [J/mol*deg K],
T : temperature in absolute scale [K].
The formula for wind farm power is as follows:
𝑃 = ∑ 𝜂𝑖(𝑡)
𝑝 𝑖 𝑀𝐴 𝑖 𝑉 𝑖
3
2𝑅𝑇 𝑖
𝑛
𝑖=1 (8)
Most often, a single farm has the same generators. Then, 𝐴𝑖 = 𝐴. It can be most often assumed with
a small error that air temperature and pressure within the farm are the same: 𝑇𝑖 = 𝑇, 𝑝𝑖 = 𝑝. Some variables
may occur in case if wind speed. The factors mentioned above are the atmospheric and measurable factors.
It is significantly more difficult to determine a momentary turbine’s efficiency, and its changes directly
influence the generator’s characteristics, which is the reason why its real process is different from the ideal
one Figure 4. The influence on efficiency can have multiple factors such as wind gradient, temperature
gradient, wind speed profile, disturbance caused by neighbouring turbines, changes in wind direction, energy
consumption on its own needs.
5. EXPLANATORY DATA SELECTION FOR ANN
The strength of neural networks is about an automatic search for dependences between input and
output data, as a result of a process called training. For effective training it is necessary to select proper input
data that is the ones which have an impact on output data, in our case, on the forecast energy generation.
The explanatory (determinant) feature of input data as well as its representativeness are equally important.
Owing to their ability to learn automatically, artificial neural networks can find information hidden in data
that is hardly accessible for other methods. As it was mentioned above, proper selection of input data is
the only condition.
The paper [7] presents the physical influence analysis of the wind speed and direction, air
temperature and atmospheric pressure on the power generated by turbines. Based on these analyses, four
forecast models of neural networks were created and tested. The conducted analyses showed that the model
which considers wind speed and temperature was most accurate. The paper [4] selects data based on
the correlation value between particular factors and energy generation per day, and on the mutual correlation
between data. On the basis of the conducted analyses, the authors selected the following explanatory data:
wind speed forecast, atmospheric pressure forecast, wind speed on a previous day, energy generation on
a previous day, average month energy generation value. The analysis of correlation between explanatory data
and energy generation is also used in papers which apply to forecasting by artificial neural networks [9] and
other forecasting methods than artificial neural networks [5].
5.1. Weathers factors
In the present paper, the selection of explanatory variables begins from the analysis of correlation
between selected atmospheric factors and the energy generation volume.The studies covered the whole wind
power plant. The analyses involved the following external factors:
5. Int J Elec & Comp Eng ISSN: 2088-8708
The quality of data and the accuracy of energy generation forecast by artificial … (Bogdan Kwiatkowski)
3961
- Wind speed at a height of 100m,
- Wind speed at the height of 99m,
- Wind speed at a height of 50m,
- Wind direction at a height of 100m,
- Wind direction at a height of 50m,
- Precipitation,
- Air pressure,
- External temperature,
- Air humidity,
The values of a correlation coefficient for the analysed factors between 2014 and 2017 were shown in
Table 1. The data analysis shows the strongest dependency of energy generation volume on the wind
speed [23]. The correlation with the other variables is significantly lower. Moreover, its value in particular
years greatly varies, therefore, it is necessary to analyse the reason of the mentioned variables before a final
decision about the selection of explanatory data is made.
Table 1. Coefficient values of correlation of explanatory variableswith energy generation volume
Explanatory variable Correlation
2014 2015 2016 2017
wind speed at a height of 100m 0.7833 0.4748 0.5577 0.5810
wind speed at a height of 99m 0.7833 0.4723 0.5543 0.5881
wind speed at a height of 50m 0.7785 0.4778 0.5586 0.6005
wind direction at a height of 100m 0.1627 0.1847 0.2963 -0.0951
wind direction at a height of 50m 0.1848 0.2063 0.3118 -0.0832
precipitation 0.1328 0.0602 0.2182 -0.0552
air pressure -0.0304 0.0550 0.2212 0.0016
external temperature -0.0350 -0.0823 -0.0013 -0.1932
air humidity 0.0706 -0.0016 0.1605 0.1590
Figures 5 and 6 showed the diagrams of correlations between wind speed and energy generation
volume. With perfect turbine operation and ideal measurement results, it should be similar to the scaled
(because of the operation of 5 turbines) characteristics of the wind turbine Figure 4. Distribution of points
only superficially resembles the mentioned characteristics. Dispersion of points may result from the changes
in the operation of particular turbines. However, the vertical lines, shown in Figure 6, for the wind speed of
0m/s, 2.4 m/s and 4.6 m/s are hard to justify other way than as incorrect measurement of wind speed or
energy generation volume. Similar situation can be noticed in diagrams with the correlation of wind speed
and energy generation volume for the years 2016 and 2017, but they appear for different speed in both cases.
Thus, the data was cleared out of values for which energy generation volumes were significantly too high for
particular wind strength. After elimination of undoubtedly incorrect measurements, wind speed correlation
coefficients at different heights and its direction with energy generation volume changed Table 2.
The analysis of coefficients of the correlation between weather parameters with energy generation
volume indicates high interdependence between energy generation volume and wind speed. The correlation
of the other parameters with energy generation volume is low. It was therefore decided that neural network
training would be conducted using two different explanatory variables:
- Wind speed at a height of 100m;
- Wind speed at a height of 100m and wind speed at a height of 50m.
Figure 5. Diagram of correlation between wind speed at a height of at a height of 100m height and energy
generation in 2014
0 5 10 15 20 25
0
2
4
6
8
10
Wind speed at a height of 100 m [m/s]
Energy[MWh]
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3957 - 3966
3962
Figure 6. Diagram of correlation between wind speed at a height of at a height of 100m height
and energy generation in 2015
Table 2. Coefficient values of correlation between explanatory variablesand energy
generation volumes after elimination of incorrect measurement
Explanatory variable
Correlation
2014 2015 2016 2017
wind speed at a height of 100m 0.7833 0.8174 0.8900 0.8839
wind speed at a height of 99m 0.7833 0.8227 0.8898 0.8840
wind speed at a height of 50m 0.7785 0.8275 0.8890 0.8823
wind direction at a height of 100m 0.1627 0.2391 0.1474 0.1314
wind direction at a height of 50m 0.1848 0.2973 0.2298 0.2006
6. RESULTS AND ANALYSIS
For the purpose of evaluation and comparison of the quality of conducted forecasts, the following
indices were introduced:
- Mean absolute error:
𝑀𝐴𝐸 =
1
𝑛
∑ |𝑒 𝑘|𝑛
𝑘=1 (9)
- Normed root mean square error
𝑛𝑅𝑀𝑆𝐸 =
√
∑ 𝑒 𝑘
2𝑛
𝑘=1
𝑛
𝑃 𝑁
(10)
- Percentage effectiveness of obtaining forecast with the accuracy of 2MW which is 20% of installed
capacity
𝐴𝐸𝑀𝐸2 =
count(𝐾2)
𝑛
∗ 100%, 𝐾2 = {𝑘: |𝑒 𝑘| < 2} (11)
- Percentage effectiveness of obtaining forecast with the accuracy of 1MW which is 10% of installed
capacity
𝐴𝐸𝑀𝐸1 =
count(𝐾1)
𝑛
∗ 100%, 𝐾1 = {𝑘: |𝑒 𝑘| < 2} (12)
- Maximum forecast error
𝑀𝑎𝑥𝐸 = min
k=1..n
(𝑒 𝑘) (13)
where:
𝑛 : number of forecasts,
𝑒 𝑘 : error of k-th forecast,
𝑃 𝑁 : rated farm capacity
0 5 10 15 20 25
0
2
4
6
8
10
Wind speed at a height of 100 m [m/s]
Energy[MWh]
7. Int J Elec & Comp Eng ISSN: 2088-8708
The quality of data and the accuracy of energy generation forecast by artificial … (Bogdan Kwiatkowski)
3963
6.1. Forecasts with conservative method
In the conservative method an approach with the perspective was applied:
- 1h -forecast based on the generation an hour before,
- 6h - forecast based on the generation 6 hours before.
Figure7 showed forecast results for the perspective of 1h. Very short time perspective makes
a visual impression that the waveforms practically coincide with each other. However, the values of indices
presented in Table 3, which estimate forecast accuracy show that the error is not so small. Attention is
brought to a low value of mean absolute error (MAE) and a relatively high value of maximum absolute error
(MaxE). Figure 8 shows the forecast results for the perspective of 6h and Table 4 includes the values of
indices for estimating forecasts accuracy. Time perspective extension significantly worsened forecasts
accuracy-mean error increased more than 100%, while maximum absolute error reached the value close to
the rated power of electric power plant Table 4.
Figure 7. Prediction of energy generation with conservative method in the perspective of 1h:
forecast-interrupt line, real generation-continuous line, forecast error-dot line
Table 3. Obtained results of prediction with conservative method for 1h ahead
MAE
[MW]
nRMSE
[%]
AEME2
[%]
AEME1
[%]
MaxE
[MW]
2014 0.74 11.1 92.0 73.9 5.77
2015 0.57 9.4 94.2 82.0 8.31
2016 0.59 10.0 93.0 81.8 7.11
2017 0.62 10.1 93.1 79.7 7.48
Figure 8. Prediction of energy generation with conservative method in the perspective of 6h:
forecast-interrupt line, real generation-continuous line, forecast error-dot line
Table 4. Obtained prediction results with conservative method for 6h ahead
MAE
[MW]
nRMSE
[%]
AEME2
[%]
AEME1
[%]
MaxE
[MW]
2014 1.47 22.2 73.4 55.1 9.59
2015 1.34 20.3 76.3 57.9 9.66
2016 1.32 20.5 77.0 59.3 9.36
2017 1.45 21.4 74.2 54.0 9.52
6.2. Forecasts with neural network
Artificial neural network was conducted using the data presented in p.5. The trained network was
used for forecasting based on current weather, volume of energy produced by energy wind generators. Data
8. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3957 - 3966
3964
used for the training come from the period 01.01.2014-31.12.2014, while test data from the period
01.01.2015-31.12.2017. Forecasts were conducted for the whole year and for particular months. Network
trained with data from January 2014 was used for forecasting the energy generation in January in the years
2015-2017, and from February in the following years, etc.
The kind of problem and the structure of data indicate that studies should begin the selection of
SNN architecture from multilayer feedforward networks. The literature, in case of feedforward networks,
indicates that a network comprising 3 layers: input, hidden and output, is able to solve every problem.
A remaining problem to solve is the number of neurons in particular levels. In case of input and output layers
the answers are obvious:
- The amount of neurons in the input layer must be equal to the length of input vector-in our case, it will be
1 neuron, when the training is performed based only on the speed at a height of 100m or 2 neurons, when
the training uses the speed at the heights of 100m and 50m.
- The amount of neurons in the output layer must be equal to the amount of forecast values-in our case,
it the amount of generated energy, so 1 neuron is sufficient.
Determining the length of hidden layer, in turn, is not so obvious. Although there are some
dependencies used for this purpose but the literature indicates that calculated values should be treated as
minimum values, and the selection should be performed by trial and error. In the discussed case, the tests
were conducted for the hidden layer comprising 2, 3, 4 and 5 neurons. It should be emphasised that an
excessive increase in length of hidden layer may lead to an over training which results in very good training
results and a significant drop in performance of network with the data analysis outside the training set.
According to the assumptions of feedforward network architecture, all neurons in a layer have
the same transition function. It was decided that in the input and output layers a linear function would be
used, and in the hidden layer, tangent curve function. The trained network with Levenberga-Marqurdta
method. Network training and tests were carried out in the Matlab env., equipped with the toolbox Neural
Networks that offers a wide range of architectures and procedures used for training the ANN [24, 25].
Visually, similar results were obtained for the annual forecast, in both analysed cases-forecasts
based on wind speed at a height of 100m and forecasts based on wind speed at of 100m and 50m as shown in
Figures 9 and 10. Tested accuracy coefficients have similar values as shown in Tables 5 and 6. Mean
absolute error for the first case is 0.72MW, while for the second one 0.76MW. Allowing forecast deviation
from real values of 20%, the network effectiveness is 93.7% for the first case and 92.2% for the second case.
With forecast deviation of 10%, it is 72.7% and 72%, respectively. Obtained values are comparable, but a bit
better for the forecasts that use only wind speed at a height of 100m.
Figure 9. Annual forecast of energy generation realized by ANN based on wind speed at a height
of 100m, forecast-interrupt line, real generation-ccontinuous line, forecast error-dot line
Figure 10. Annual energy realized by ANN based on wind speed at a height of 100m and 50m,
forecast-interrupt line, real generation-continuous line, forecast error-dot line
9. Int J Elec & Comp Eng ISSN: 2088-8708
The quality of data and the accuracy of energy generation forecast by artificial … (Bogdan Kwiatkowski)
3965
Table 5. Obtained data of SSN prediction, training with data from 2012,
wind only at a height of 100m, 3 neurons
MAE
[MW]
nRMSE
[%]
AEME2
[%]
AEME1
[%]
MaxE
[MW]
2015 0.70 10.8 92.8 76.7 6.26
2016 0.77 10.6 94.9 67.0 4.83
2017 0.70 10.2 93.4 74.5 7.47
Table 6 Obtained data of SSN prediction, training with data from 2013,
wind at a height of 100m and 50m, 4 neurons.
MAE
[MW]
nRMSE
[%]
AEME2
[%]
AEME1
[%]
MaxE
[MW]
2015 0.68 10.2 93.7 76.8 5.89
2016 0.76 10.4 93.3 68.9 5.16
2017 0.83 12.2 89.5 70.4 7.29
In case of monthly forecasts, due to cyclical character of atmospheric conditions, data can be more
coherent which should result in greater accuracy of prediction. The obtained forecasts results prove these
assumptions as shown in Figure 11. All accuracy prediction indices improved Table 7. The important thing is
that maximum forecast error decreased to the value of about 3.85MW which is the value below 40% of rated
power of electric power plant. The results from February 2015 went particularly well-mean absolute error
was 0.34MW, root mean square error was 5.4% and 92.8% of forecasts did not differ of more than 10% from
real values.
Figure 11. Energy generation forecast (February) realized by ANN based on wind speed at a height of
100m, monthly interrupt line, real generation – continuous line, forecast error – dot line
Table 7 Obtained results of monthly (February) prediction of SSN
training with data from 2014, wind only at a height of 100m, 3 neurons
MAE
[MW]
nRMSE
[%]
AEME2
[%]
AEME1
[%]
MaxE
[MW]
2015 0.34 5.4 99.1 92.8 3.80
2016 0.60 8.6 95.7 78.6 3.85
2017 0.58 8.6 95.6 78.6 3.88
7. CONCLUSION
The paper discussed 4 models: conservative with 1h perspective, conservative with 6h perspective,
neural network that used wind speed at a height of 100m, neural network that used wind speed at a height of
100m and 50m. The highest accuracy was obtained for neural network that used wind speed at a height of
100m with monthly forecasts. The studies proved known from literature phenomenon that artificial neural
networks can be effectively used in tasks of forecasting energy generation.The comparison of forecasts
results obtained using SSN with conservative method indicates that networks are more universal. Forecast
accuracy of conservative method decreases along with the extension of time perspective.
Forecast accuracy of forecasts with artificial neural networks strongly depends on the selection of
explanatory data. In the analysed case, the influence of 9 different data were tested-it turned out that only
wind speed has a real impact on energy generation volume. Of course, it does not mean that other than
10. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3957 - 3966
3966
analysed data fail to influence the forecast value. At the same time, it cannot be eliminated that by different
input data coding, their correlation with energy production will improve.
The quality of explanatory data (measurements) is very crucial for the forecast accuracy. In case of
artificial neural networks, mendacious data aggravate the training and, regardless of prediction method,
disenable correct forecasts evaluation. In case of ANN, reducing the range of data to one month had
a positive impact on the forecast accuracy. Training performed for data from the whole year and, next,
forecasting the generation for the whole year gives worse results than training with data from one month and
forecasting for the same month in following years. The changes in the turbine operation resulting from
weather and operating conditions inhibit the neural network training process, as it seems that they could be
partly identified by introducing additional explanatory variables.
REFERENCES
[1] S. S. Sakthi, et al., "Wind Integrated Thermal Unit Commitment Solution using Grey Wolf Optimizer,"
International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2309-2320, 2017.
[2] D. Sobczyński, "Simulation study of small isolated power electronic converters for PV system," 2016 Progress in
Applied Electrical Engineering (PAEE), Koscielisko-Zakopane, pp. 1-6, 2016.
[3] I. M. Wartana, et al., "Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability
Constraint," International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2329-2337, 2017.
[4] D. Baczyński and P. Piotrowski, "Prognozowanie dobowej produkcji energii elektrycznej przez turbinę wiatrową z
horyzontem 1 doby," Przegląd Elektrotechniczny, vol. 90, no. 9, pp. 113-117, 2014.
[5] T. Hossa, W. Sokołowska, K., Fabisz and A., Filipowska, "Prognozowanie generacji wiatrowej z wykorzystaniem
metod lokalnych i regresji nieliniowej," Rynek energii, vol. 111, no. 2, pp. 61-68, 2014.
[6] G. Sideratos and N.D. Hatziargyriou, "An Advanced Statistical Method for Wind Power Forecasting," IEEE
Transactions on Power Systems, vol. 22, no. 1, pp. 258-265, 2007.
[7] Z. Hanzelka, M. Dutka and B. Świątek, "Prognozowanie ilości generowanej energii elektrycznej przez elektrownie
wiatrowe z wykorzystaniem sieci neuronowych," Leonardo Energy, pp. 1- 9, 2016.
[8] W. Y. Chang, "Comparison of Three Short Term Wind Power Forecasting," Advanced Materials Research, vol. 684,
pp. 671-675, 2013.
[9] Y. Hong, T. Yu and Ch. Liu, "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode
Decomposition," Energies, vol. 6, no. 12, pp. 6137-6152, 2013.
[10] W.Y. Chang, "Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based
Hybrid Method," Energies, vol. 6, no. 9, pp. 4879-4896, 2013.
[11] J. Shi, J. M. Guo and S. T. Zheng, "Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power
Generation Time Series," Renewable and Sustainable Energy Reviews, vol. 16, no. 5, pp. 3471-3480, 2012.
[12] J. Wang, et al, "Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm
optimisation: a case study of wind speed time series," IET Renewable Power Generation, vol. 10, no. 3,
pp. 287-298, 2016.
[13] Z. Gomolka, E. Dudek-Dyduch and Y. Kondratenko, "From homogeneous network to neural nets with fractional derivative
mechanism," International Conference on Artificial Intelligence and Soft Computing-ICAISC, vol. 10245, pp. 52-63, 2017.
[14] T. Dhert, T. Ashuri and J. Martins, "Aerodynamic shape optimization of wind turbine blades using a Reynolds-
averaged Navier-Stokes model and an adjoint method," Wind Energy, vol. 20, no. 5, pp. 909–926, 2016.
[15] A. Badawi, N. F. Hasbullaha, S. Yusoff, S. Khan, A. Hashim, A. Zyoud, M. Elamassie, "Evaluation of wind power
for electrical energy generation in the mediterranean coast of Palestine for 14 years," International Journal of
Electrical and Computer Engineering (IJECE), vol. 9, no. 4, pp. 2212-2219, 2019
[16] G.K. Venayagamoorthy, et al., "One Step Ahead. Short-Term Wind Power Forecasting and Intelligent Predictive
Control Based on Data Analytics," IEEE Power and Energy Magazine, vol. 10, no. 5, pp. 70-78, 2012.
[17] A.A. Moghaddam and A.R. Seifi, "Study of forecasting renewable energies in smart grids using linear predictiove
filters and neural networks," IET Renewable Power Generation, vol. 5, no. 6, pp. 470-480, 2011.
[18] Z. Gomółka, et al., "Improvement of Image Processing by Using Homogeneus Neural Networks with Fractional
Derivatives Theorem," Dynamical Systems, Differential Equations and Applcations, vol. 31, pp. 505-514, 2011.
[19] D. O. Hebb, "The organization of behavior," Psychology Press; 1 edition, 1949.
[20] N.K. Singh, A.K. Singh abd M. Tripathy, "Selection of hidden layer neurons and best training method for FFNN in
application of long term load forecasting," Journal of Electrical Engineering, vol. 63, no. 3, pp. 153-161, 2012.
[21] J. Bartman, "Reguła PID uczenia sztucznych neuronów," Metody Informatyki Stosowanej, vol. 20, no. 3, pp. 5-19, 2009.
[22] A. Bielecki, "Mathematical model of architecture and learning processes of artificial neural networks," Task
quarterly, vol. 7, no. 1, pp. 93–114, 2003.
[23] W. Malska and D. Mazur, "Analiza wpływu prędkości wiatru nagenerację mocy naprzykładzie farmy wiatrowej,"
Przegląd Elektrotechniczny, vol. 1, no. 4, pp. 56-59, 2017.
[24] J. Bartman, Z., Gomółka and B. Twaróg, "ANN training-the analysis of the selected procedures in Matlab
environment, " Computing in Science and Technology, pp. 88-101, 2015.
[25] S. B. A. Kamaruddin, N. A. M. Ghani, H. A. Rahim and I. Musirin, "Killer whale-backpropagation (KW-BP)
algorithm for accuracy improvement of neural network forecasting models on energyefficient data," International
Journal of Artificial Intelligence (IJA-AI), vol. 8, no. 3, pp. 270-277, 2019.