This document presents a performance comparison of artificial intelligence techniques for short-term photovoltaic (PV) current forecasting. Artificial neural network (ANN) and random forest (RF) models are used to forecast PV output current for the next 24 hours based on hourly solar irradiance, temperature, hour, power, and current data from a case study in Malaysia. Both ANN and RF are able to forecast future hourly PV current with errors reduced after the input data is preprocessed using wavelet decomposition and multiple time lags. The ANN model uses Levenberg-Marquardt optimization and RF uses bagging and bootstrapping. Evaluation metrics show that ANN and RF at different numbers of trees can accurately
Short-term wind speed forecasting system using deep learning for wind turbine...IJECEIAES
It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
Tdtd-Edr: Time Orient Delay Tolerant Density Estimation Technique Based Data ...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Support Vector Machine for Wind Speed PredictionIJRST Journal
The energy is a vital input for the social and economic development of any nation. With increasing agricultural and industrial activities in the country, the demand for energy is also increasing. The increasing use of natural and renewable energy sources is needed to take the burden of our current dependency on fossil fuels. Development and analysis of renewable energy models helps utility in energy forecasting, planning, research and policy making. The wind power is a clean, inexhaustible, and almost a free source of energy. But the integration of wind parks with the power grid has resulted in many challenges for the utility in terms of commitment and control of power plants. As wind speed and wind direction fluctuate frequently, the accurate long-term and short-term forecasting of wind speed is important for ascertaining the wind power generation availability. To deal with wind speed forecasting, many methods have been developed such as physical method, which use lots of physical considerations to reach the best forecasting precision and other is the statistical method, which specializes in finding the relationship of the measured power data. Wind speed can be predicted by using time series analysis, artificial neural network, Kalman Filter method, linear prediction method, spatial correlation models and wavelet, also by using the support vector machines. In this paper, the SVM is used for day ahead prediction of wind speed using historical data of wind park. In this paper Support Vector Machine (SVM) results are compared with feedforward Backpropagation neural network. It is observed that the Mean Absolute Percentage Error (MAPE) by SVM method is around 7% and correlation coefficient is close to 1. This justifies the ability of SVM for wind speed prediction task than Backpropagation algorithm.
A Novel Technique to Enhance the Lifetime of Wireless Sensor Networks through...IJECEIAES
In the most of the real world scenarios, wireless sensor networks are used. Some of the major tasks of these types of networks is to sense some information and sending it to monitoring system or tracking some activity etc. In such applications, the sensor nodes are deployed in large area and in considerably large numbers [1]-[3]. Each of these node will be having constrained resources whether it might be energy, memory or processing capability. Energy is the major resource constraint in these types of networks. Hence enough care to be taken in all aspects such that energy can be used very efficiently. Different Activities which will be taking place in a sensor node are sensing, radio operations and receiving and computing. Among all these operations, radio consumes maximum power. Hence there is a need of reducing the power consumption in such radio operations. In the proposed work a software module is developed which will reduce the number of transmissions done to the base station. The work compares the consecutively sensed data and if these data are same then the old data then the old data will be retained. In other case the newly sensed data will be sent to the sink node. This technique reduces the number of data transmissions in a significant way. With the reduced number of transmissions, the energy saved in each node will be more, which will increase the lifetime of the entire network.
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.
SENSOR SELECTION SCHEME IN WIRELESS SENSOR NETWORKS: A NEW ROUTING APPROACHcsandit
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless
sensor networks, based on data mining formulation. The proposed adapting routing scheme for
sensors for achieving energy efficiency. The experimental validation of the proposed approach
using publicly available Intel Berkeley lab Wireless Sensor Network dataset shows that it is
possible to achieve energy efficient environment monitoring for wireless sensor networks, with a
trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
Synchrophasor Applications Facilitating Interactions between Transmission and...Luigi Vanfretti
Distribution grid dynamics will become increasingly complex due to the transition from passive to active networks arising from the increase of renewable energy sources at medium and voltage level. A successful transition requires to increase the observability and awareness of the interactions between Transmission and Distribution (T&D) grids, particularly to guarantee adequate operational security.
This presentation explores how different technical means can facilitate interactions between TSOs and DSOs with the utilization of GPS-time-synchronized phasor measurements (aka Phasor Measurement Units (PMUs)) with millisecond resolution. If made available in actual T&D networks, such high-sampled data across operational boundaries allows an opportunity to extract information related to different time-scales.
As part of the work carried out in the EU-funded FP7 IDE4L project (http://ide4l.eu/), a specific use case, containing PMU-based monitoring functions, has been defined to support the architecture design of future distribution grid automation systems. As a result, the architecture can accommodate for key dynamic information extraction and exchange between DSO and TSO.
This presentation presents the use case and focuses on the technical aspects related to the development and implementation of the PMU-based monitoring functionalities that can provide means to facilitate technical co-operation between transmission and distribution operations.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Short-term wind speed forecasting system using deep learning for wind turbine...IJECEIAES
It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
Tdtd-Edr: Time Orient Delay Tolerant Density Estimation Technique Based Data ...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Support Vector Machine for Wind Speed PredictionIJRST Journal
The energy is a vital input for the social and economic development of any nation. With increasing agricultural and industrial activities in the country, the demand for energy is also increasing. The increasing use of natural and renewable energy sources is needed to take the burden of our current dependency on fossil fuels. Development and analysis of renewable energy models helps utility in energy forecasting, planning, research and policy making. The wind power is a clean, inexhaustible, and almost a free source of energy. But the integration of wind parks with the power grid has resulted in many challenges for the utility in terms of commitment and control of power plants. As wind speed and wind direction fluctuate frequently, the accurate long-term and short-term forecasting of wind speed is important for ascertaining the wind power generation availability. To deal with wind speed forecasting, many methods have been developed such as physical method, which use lots of physical considerations to reach the best forecasting precision and other is the statistical method, which specializes in finding the relationship of the measured power data. Wind speed can be predicted by using time series analysis, artificial neural network, Kalman Filter method, linear prediction method, spatial correlation models and wavelet, also by using the support vector machines. In this paper, the SVM is used for day ahead prediction of wind speed using historical data of wind park. In this paper Support Vector Machine (SVM) results are compared with feedforward Backpropagation neural network. It is observed that the Mean Absolute Percentage Error (MAPE) by SVM method is around 7% and correlation coefficient is close to 1. This justifies the ability of SVM for wind speed prediction task than Backpropagation algorithm.
A Novel Technique to Enhance the Lifetime of Wireless Sensor Networks through...IJECEIAES
In the most of the real world scenarios, wireless sensor networks are used. Some of the major tasks of these types of networks is to sense some information and sending it to monitoring system or tracking some activity etc. In such applications, the sensor nodes are deployed in large area and in considerably large numbers [1]-[3]. Each of these node will be having constrained resources whether it might be energy, memory or processing capability. Energy is the major resource constraint in these types of networks. Hence enough care to be taken in all aspects such that energy can be used very efficiently. Different Activities which will be taking place in a sensor node are sensing, radio operations and receiving and computing. Among all these operations, radio consumes maximum power. Hence there is a need of reducing the power consumption in such radio operations. In the proposed work a software module is developed which will reduce the number of transmissions done to the base station. The work compares the consecutively sensed data and if these data are same then the old data then the old data will be retained. In other case the newly sensed data will be sent to the sink node. This technique reduces the number of data transmissions in a significant way. With the reduced number of transmissions, the energy saved in each node will be more, which will increase the lifetime of the entire network.
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.
SENSOR SELECTION SCHEME IN WIRELESS SENSOR NETWORKS: A NEW ROUTING APPROACHcsandit
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless
sensor networks, based on data mining formulation. The proposed adapting routing scheme for
sensors for achieving energy efficiency. The experimental validation of the proposed approach
using publicly available Intel Berkeley lab Wireless Sensor Network dataset shows that it is
possible to achieve energy efficient environment monitoring for wireless sensor networks, with a
trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
Synchrophasor Applications Facilitating Interactions between Transmission and...Luigi Vanfretti
Distribution grid dynamics will become increasingly complex due to the transition from passive to active networks arising from the increase of renewable energy sources at medium and voltage level. A successful transition requires to increase the observability and awareness of the interactions between Transmission and Distribution (T&D) grids, particularly to guarantee adequate operational security.
This presentation explores how different technical means can facilitate interactions between TSOs and DSOs with the utilization of GPS-time-synchronized phasor measurements (aka Phasor Measurement Units (PMUs)) with millisecond resolution. If made available in actual T&D networks, such high-sampled data across operational boundaries allows an opportunity to extract information related to different time-scales.
As part of the work carried out in the EU-funded FP7 IDE4L project (http://ide4l.eu/), a specific use case, containing PMU-based monitoring functions, has been defined to support the architecture design of future distribution grid automation systems. As a result, the architecture can accommodate for key dynamic information extraction and exchange between DSO and TSO.
This presentation presents the use case and focuses on the technical aspects related to the development and implementation of the PMU-based monitoring functionalities that can provide means to facilitate technical co-operation between transmission and distribution operations.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Voltage stability Analysis using GridCalAnmol Dwivedi
Power system voltage stability is characterized as being capable of maintaining load voltage magnitudes within specified operating limits under steady state conditions. This presentation deals with the modeling of two standard power systems test cases i.e the Nordic-32 and the Nordic-68, comparing the power flows results obtained from GridCal against PSS/E, finding the respective P-V curves for the two test cases using the continuation power flow under contingencies, and finally proposing a graph-based test statistic which can be used for an imminent voltage instability. The simulations are carried out using an open-source power system software called GridCal and the scripts for this project are written in python.
SENSOR SELECTION SCHEME IN TEMPERATURE WIRELESS SENSOR NETWORKijwmn
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless sensor
networks, based on data mining formulation. The proposed adapting routing scheme for sensors for
achieving energy efficiency from temperature wireless sensor network data set. The experimental
validation of the proposed approach using publicly available Intel Berkeley lab Wireless Sensor Network
dataset shows that it is possible to achieve energy efficient environment monitoring for wireless sensor
networks, with a trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
Monitoring of Transmission and Distribution Grids using PMUsLuigi Vanfretti
My presentation on "Monitoring of Transmission and Distribution Grids using PMUs" for the Workshop on Energy Business Opportunities in NY State.
The Center for Integrated Electrical Energy Systems (CIEES) at Stony Brook University and the Center for Future Energy Systems (CFES) at Rensselaer Polytechnic Institute will be holding a one day Workshop on Energy Business Opportunities in NY State.
Experimental Testing of a Real-Time Implementation of a PMU-Based Wide-Area D...Power System Operation
The modern power grid is being used under operating conditions of increasing stress, giving
rise to grid stability issues. One of these stability issues is the phenomenon of inter-area oscillations.
Simulations have demonstrated the advantages of Wide-area Measurement Signals (WAMS)-based Oscillation Damping Controls in achieving improved electromechanical mode damping compared to traditional,
local signal-based Power System Stabilizers (PSS). This work takes an existing Phasor-based oscillation
damping (POD) algorithm and uses it to implement a proof-of-concept, wide-area, real-time controller
on National Instruments hardware. The developed prototype is tested in a real-time Hardware-in-theloop setup (RT-HIL) using OPAL-RT’s eMEGASIM real-time simulation platform and synchrophasor data
from actual Phasor Measurement Units (PMUs). The prototype and experiments provide insight into the
feasibility and real-world limitations of wide-area controls. Further, it is demonstrated how the proposed
control architecture has applications independent of the controlled power system device. Challenges faced,
the solutions implemented together with the present prototype’s limitations are also discussed.
Wireless communication without pre shared secrets using spread spectrum techn...eSAT Journals
Abstract
The wireless communication using spread spectrum relies on the assumption that some secret is shared among source and
destination node before communication or transmission has started. This problem is called the circular dependency problem
(CDP). This CDP exists in large networks, where nodes frequently join and leaves the network. In this work we have introduced
an efficient and reliable mechanism called Advanced Encryption Standard (AES) Algorithm, to overcome circular dependency
problem (CDP). This is an efficient algorithm to make successful transmission of data without pre-sharing any secret key. We
have evaluated this by simulation in Matrix Laboratory (MATLAB).
Keywords: -Spread spectrum, CDP, AES and MATLAB.
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...Luigi Vanfretti
This talk starts by exploring how electrical power systems are increasingly becoming digitalized, leading to their transformation into a class of cyber-physical systems (a system of systems) where the electrical grid merges with ubiquitous information and communication technologies (ICT).
This type of complex systems present unprecedented challenges in their operation and control, and due to unknown interactions with ICT, require new concepts, methods and tools to facilitate their operational design, manufacturing (of components), and testing/verification/validation of their performance.
Inspired by the tremendous advantages of the model-based system engineering (MBSE) framework developed by the aerospace and military communities, this talk will highlight the challenges to adopt MBSE for electrical power grids. MBSE is not only a framework to deal with all the phases of putting in place complex systems-of-systems, but also provides a foundation for the democratization of technology - both software and hardware.
The talk will illustrate the foundations that have been built by the presenter's research over the last 7 years, placed within the context of MBSE, with focus on areas of power engineering. Some of these foundations and contributions include the OpenIPSL, RaPId, SD3K, BableFish and Khorjin open source software developed and distributed online by the research group, and available at: https://github.com/ALSETLab
An Ant colony optimization algorithm to solve the broken link problem in wire...IJERA Editor
Aco is a well –known metahuristic in which a colony of artificial ants cooperates in explain Good solution to a combinational optimization problem. Wireless sensor consisting of nodes with limited power is deployed to gather useful information From the field. In wireless sensor network it is critical to collect the information in an energy efficient Manner.ant colony optimization, a swarm intelligence based optimization technique, is widely used In network routing. A novel routing approach using an ant colony optimization algorithm is proposed for wireless sensor Network consisting of stable nodes illustrative example details description and cooperative performance test result the proposed approach are included. The approach is also implementing to a small sized hardware component as a router chip simulation result show that proposed algorithm Provides promising solution allowing node designers to efficiency operate routing tasks.
Wind energy forecasting using radial basis function neural networkseSAT Journals
Abstract
Wind power forecast is essential for a wind farm developer for comprehensive assessment of wind potential at a particular site or
topographical location. Wind energy potential at any given location is a non –linear function of mean average wind speed,
vertical wind profile, energy pattern factor, peak wind speed, prevailing wind direction, lull hours, air density and a few other
parameters. Wind energy pattern data of various locationsis collected from a published resource data book by Centre for Wind
Energy Technology, India.Modeling of wind energy forecasting problem consists of data collection, input-output selection,
mappingand simulation. In this work, artificial neural networks technique is adopted to deal with the wind energy forecasting
problem.After normalization, neural network will be run with training dataset.Radial Basis function based Neural Networks is a
feed-forward algorithm of artificial neural networks that offers supervised learning.It establishes local mapping with two fold
learning quickly.Wind power densities predicted for new locationsare in agreement with the measured values atthewind
monitoring stations.MAPE was found out to be less than 10% for all the values of Wind Power Density predictions at new
topographical locations and R2 is found to be nearer to unity.WPD values are multiplied by wind availability hours (generation
hours) in that particular location to give number of energy units at the turbine output. These values are compared to the output of
the wind turbine model installed in the same region, so as to assess the number of units generated by that particular wind turbine
in the respective locations.This kind of assessment is useful for wind energy projects during feasibility studies. With this work, it is
established that radial basis function neural netscan be used as a diagnostic tool for function approximation problemsconnected
towind energy resourcemodeling& forecast.
Keywords: Wind power density, wind energy, forecast, modeling, air density, peak wind speed, radial basis function,
neural network, CoD, MAPE
Convergence Problems Of Contingency Analysis In Electrical Power Transmission...CSCJournals
Contingency analysis is a tool used by power system engineers for planning and assessing
power system reliability. The conventional analytical method which is mathematical model based,
is not only tedious and time consuming in view of the large number of components in the network
but always left some critical components unassessed due to non-convergence of the power flow
analysis of such, hence the contingency analysis of such system could not be said to be
completed.
In this work, contingency analysis of line components of a standard IEEE-30 Bus and real 330-kV
Nigerian Transmission Company of Nigeria (TCN) network (28Bus) systems were investigated
using Radial Basis Function Neural Network (RBF-NN) which is artificial intelligence based.
The contingency analysis was carried out by solving the non-linear algebraic equations of steady
state model for the standard IEEE-30 Bus and TCN-28 Bus power networks using NewtonRaphson
(N-R) power flow method. RBF-NN method was used for the computation of Reactive
and Active performance indices (PIR and PIA ) which were ranked in order to reveal the criticality
of each line outage. Simulation was carried out using MATLAB R2013a version. The nonconverged
lines in both systems were reinforced and re-analysed. The results of contingency
analyses of the reinforced systems show more robust systems with complete line ranking.
Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market IJECEIAES
Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.
Impact of GPS Signal Loss and Spoofing on Power System Synchrophasor Applicat...Luigi Vanfretti
This presentation shows an experimental assessesment of the impact of time synchronization spoofing attacks (TSSA) on synchrophasor-based Wide-Area Monitoring, Protection and Control applications. Phase Angle Monitoring (PAM), anti-islanding protection and power oscillation damping applications are investigated. TSSA are created using a real-time IRIG-B signal generator and power system models are executed using a real-time simulator with commercial phasor measurement units (PMUs) coupled to them as hardware-in-the-loop. Because PMUs utilize time synchronization signals to compute synchrophasors, an error in the PMUs’ time input introduces a proportional phase error in the voltage or current phase measurements provided by the PMU. The experiments conclude that a phase angle monitoring application will show erroneous power transfers, whereas the anti-islanding protection mal-operates and the damping controller introduces negative damping in the system as a result of the time synchronization error incurred in the PMUs due to TSSA.
The proposed test-bench and TSSA approach can be used to investigate the impact of TSSA on any WAMPAC application and to determine the time synchronization error threshold that can be tolerated by these WAMPAC applications.
Time Series Data Mining - from PhD to StartupPeter Laurinec
The talk will be oriented on differences between "doing" a research and an application of time series data mining to real problems in business on a real rich data.
I will discuss, why research and business need to be related and also not. Typical tasks of time series data mining in energetics with use cases in R will be shown.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
WIND SPEED & POWER FORECASTING USING ARTIFICIAL NEURAL NETWORK (NARX) FOR NEW...Journal For Research
Continuous Depleting conventional fuel reserves and its impact as increasing global warming concerns have diverted world attention towards non-conventional energy sources. Out of different non-conventional energy sources wind energy can be consider as one of the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Today Wind energy can play a vital role to meet our energy demands; however, it faces various issues such as intermittent nature and frequency instability. To reduce such issues the knowledge of futuristic weather conditions and wind speed trend are required. This work mainly describes the implementation of NARX Artificial neural network for wind speed & power forecasting with the help of historical data available from wind farms.
Voltage stability Analysis using GridCalAnmol Dwivedi
Power system voltage stability is characterized as being capable of maintaining load voltage magnitudes within specified operating limits under steady state conditions. This presentation deals with the modeling of two standard power systems test cases i.e the Nordic-32 and the Nordic-68, comparing the power flows results obtained from GridCal against PSS/E, finding the respective P-V curves for the two test cases using the continuation power flow under contingencies, and finally proposing a graph-based test statistic which can be used for an imminent voltage instability. The simulations are carried out using an open-source power system software called GridCal and the scripts for this project are written in python.
SENSOR SELECTION SCHEME IN TEMPERATURE WIRELESS SENSOR NETWORKijwmn
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless sensor
networks, based on data mining formulation. The proposed adapting routing scheme for sensors for
achieving energy efficiency from temperature wireless sensor network data set. The experimental
validation of the proposed approach using publicly available Intel Berkeley lab Wireless Sensor Network
dataset shows that it is possible to achieve energy efficient environment monitoring for wireless sensor
networks, with a trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
Monitoring of Transmission and Distribution Grids using PMUsLuigi Vanfretti
My presentation on "Monitoring of Transmission and Distribution Grids using PMUs" for the Workshop on Energy Business Opportunities in NY State.
The Center for Integrated Electrical Energy Systems (CIEES) at Stony Brook University and the Center for Future Energy Systems (CFES) at Rensselaer Polytechnic Institute will be holding a one day Workshop on Energy Business Opportunities in NY State.
Experimental Testing of a Real-Time Implementation of a PMU-Based Wide-Area D...Power System Operation
The modern power grid is being used under operating conditions of increasing stress, giving
rise to grid stability issues. One of these stability issues is the phenomenon of inter-area oscillations.
Simulations have demonstrated the advantages of Wide-area Measurement Signals (WAMS)-based Oscillation Damping Controls in achieving improved electromechanical mode damping compared to traditional,
local signal-based Power System Stabilizers (PSS). This work takes an existing Phasor-based oscillation
damping (POD) algorithm and uses it to implement a proof-of-concept, wide-area, real-time controller
on National Instruments hardware. The developed prototype is tested in a real-time Hardware-in-theloop setup (RT-HIL) using OPAL-RT’s eMEGASIM real-time simulation platform and synchrophasor data
from actual Phasor Measurement Units (PMUs). The prototype and experiments provide insight into the
feasibility and real-world limitations of wide-area controls. Further, it is demonstrated how the proposed
control architecture has applications independent of the controlled power system device. Challenges faced,
the solutions implemented together with the present prototype’s limitations are also discussed.
Wireless communication without pre shared secrets using spread spectrum techn...eSAT Journals
Abstract
The wireless communication using spread spectrum relies on the assumption that some secret is shared among source and
destination node before communication or transmission has started. This problem is called the circular dependency problem
(CDP). This CDP exists in large networks, where nodes frequently join and leaves the network. In this work we have introduced
an efficient and reliable mechanism called Advanced Encryption Standard (AES) Algorithm, to overcome circular dependency
problem (CDP). This is an efficient algorithm to make successful transmission of data without pre-sharing any secret key. We
have evaluated this by simulation in Matrix Laboratory (MATLAB).
Keywords: -Spread spectrum, CDP, AES and MATLAB.
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...Luigi Vanfretti
This talk starts by exploring how electrical power systems are increasingly becoming digitalized, leading to their transformation into a class of cyber-physical systems (a system of systems) where the electrical grid merges with ubiquitous information and communication technologies (ICT).
This type of complex systems present unprecedented challenges in their operation and control, and due to unknown interactions with ICT, require new concepts, methods and tools to facilitate their operational design, manufacturing (of components), and testing/verification/validation of their performance.
Inspired by the tremendous advantages of the model-based system engineering (MBSE) framework developed by the aerospace and military communities, this talk will highlight the challenges to adopt MBSE for electrical power grids. MBSE is not only a framework to deal with all the phases of putting in place complex systems-of-systems, but also provides a foundation for the democratization of technology - both software and hardware.
The talk will illustrate the foundations that have been built by the presenter's research over the last 7 years, placed within the context of MBSE, with focus on areas of power engineering. Some of these foundations and contributions include the OpenIPSL, RaPId, SD3K, BableFish and Khorjin open source software developed and distributed online by the research group, and available at: https://github.com/ALSETLab
An Ant colony optimization algorithm to solve the broken link problem in wire...IJERA Editor
Aco is a well –known metahuristic in which a colony of artificial ants cooperates in explain Good solution to a combinational optimization problem. Wireless sensor consisting of nodes with limited power is deployed to gather useful information From the field. In wireless sensor network it is critical to collect the information in an energy efficient Manner.ant colony optimization, a swarm intelligence based optimization technique, is widely used In network routing. A novel routing approach using an ant colony optimization algorithm is proposed for wireless sensor Network consisting of stable nodes illustrative example details description and cooperative performance test result the proposed approach are included. The approach is also implementing to a small sized hardware component as a router chip simulation result show that proposed algorithm Provides promising solution allowing node designers to efficiency operate routing tasks.
Wind energy forecasting using radial basis function neural networkseSAT Journals
Abstract
Wind power forecast is essential for a wind farm developer for comprehensive assessment of wind potential at a particular site or
topographical location. Wind energy potential at any given location is a non –linear function of mean average wind speed,
vertical wind profile, energy pattern factor, peak wind speed, prevailing wind direction, lull hours, air density and a few other
parameters. Wind energy pattern data of various locationsis collected from a published resource data book by Centre for Wind
Energy Technology, India.Modeling of wind energy forecasting problem consists of data collection, input-output selection,
mappingand simulation. In this work, artificial neural networks technique is adopted to deal with the wind energy forecasting
problem.After normalization, neural network will be run with training dataset.Radial Basis function based Neural Networks is a
feed-forward algorithm of artificial neural networks that offers supervised learning.It establishes local mapping with two fold
learning quickly.Wind power densities predicted for new locationsare in agreement with the measured values atthewind
monitoring stations.MAPE was found out to be less than 10% for all the values of Wind Power Density predictions at new
topographical locations and R2 is found to be nearer to unity.WPD values are multiplied by wind availability hours (generation
hours) in that particular location to give number of energy units at the turbine output. These values are compared to the output of
the wind turbine model installed in the same region, so as to assess the number of units generated by that particular wind turbine
in the respective locations.This kind of assessment is useful for wind energy projects during feasibility studies. With this work, it is
established that radial basis function neural netscan be used as a diagnostic tool for function approximation problemsconnected
towind energy resourcemodeling& forecast.
Keywords: Wind power density, wind energy, forecast, modeling, air density, peak wind speed, radial basis function,
neural network, CoD, MAPE
Convergence Problems Of Contingency Analysis In Electrical Power Transmission...CSCJournals
Contingency analysis is a tool used by power system engineers for planning and assessing
power system reliability. The conventional analytical method which is mathematical model based,
is not only tedious and time consuming in view of the large number of components in the network
but always left some critical components unassessed due to non-convergence of the power flow
analysis of such, hence the contingency analysis of such system could not be said to be
completed.
In this work, contingency analysis of line components of a standard IEEE-30 Bus and real 330-kV
Nigerian Transmission Company of Nigeria (TCN) network (28Bus) systems were investigated
using Radial Basis Function Neural Network (RBF-NN) which is artificial intelligence based.
The contingency analysis was carried out by solving the non-linear algebraic equations of steady
state model for the standard IEEE-30 Bus and TCN-28 Bus power networks using NewtonRaphson
(N-R) power flow method. RBF-NN method was used for the computation of Reactive
and Active performance indices (PIR and PIA ) which were ranked in order to reveal the criticality
of each line outage. Simulation was carried out using MATLAB R2013a version. The nonconverged
lines in both systems were reinforced and re-analysed. The results of contingency
analyses of the reinforced systems show more robust systems with complete line ranking.
Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market IJECEIAES
Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.
Impact of GPS Signal Loss and Spoofing on Power System Synchrophasor Applicat...Luigi Vanfretti
This presentation shows an experimental assessesment of the impact of time synchronization spoofing attacks (TSSA) on synchrophasor-based Wide-Area Monitoring, Protection and Control applications. Phase Angle Monitoring (PAM), anti-islanding protection and power oscillation damping applications are investigated. TSSA are created using a real-time IRIG-B signal generator and power system models are executed using a real-time simulator with commercial phasor measurement units (PMUs) coupled to them as hardware-in-the-loop. Because PMUs utilize time synchronization signals to compute synchrophasors, an error in the PMUs’ time input introduces a proportional phase error in the voltage or current phase measurements provided by the PMU. The experiments conclude that a phase angle monitoring application will show erroneous power transfers, whereas the anti-islanding protection mal-operates and the damping controller introduces negative damping in the system as a result of the time synchronization error incurred in the PMUs due to TSSA.
The proposed test-bench and TSSA approach can be used to investigate the impact of TSSA on any WAMPAC application and to determine the time synchronization error threshold that can be tolerated by these WAMPAC applications.
Time Series Data Mining - from PhD to StartupPeter Laurinec
The talk will be oriented on differences between "doing" a research and an application of time series data mining to real problems in business on a real rich data.
I will discuss, why research and business need to be related and also not. Typical tasks of time series data mining in energetics with use cases in R will be shown.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
WIND SPEED & POWER FORECASTING USING ARTIFICIAL NEURAL NETWORK (NARX) FOR NEW...Journal For Research
Continuous Depleting conventional fuel reserves and its impact as increasing global warming concerns have diverted world attention towards non-conventional energy sources. Out of different non-conventional energy sources wind energy can be consider as one of the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Today Wind energy can play a vital role to meet our energy demands; however, it faces various issues such as intermittent nature and frequency instability. To reduce such issues the knowledge of futuristic weather conditions and wind speed trend are required. This work mainly describes the implementation of NARX Artificial neural network for wind speed & power forecasting with the help of historical data available from wind farms.
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
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.
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.
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.
IEEE International Conference PresentationAnmol Dwivedi
IEEE INTERNATIONAL CONFERENCE -
Paper Title "Real-Time Implementation of Phasor Measurement Unit Using NI CompactRIO".
Code Available on: https://github.com/anmold-07/Synchrophasor-Estimation
ESPRIT Method Enhancement for Real-time Wind Turbine Fault RecognitionIAES-IJPEDS
Early fault diagnosis plays a very important role in the modern energy production systems. The wind turbine machine requires a regular maintenance to guarantee an acceptable lifetime and to minimize production loss. In order to implement a fast, proactive condition monitoring, ESPRIT- TLS method seems the correct choice due to its robustness in improving the frequency and amplitude detection. Nevertheless, it has a very complex computation to implement in real time. To avoid this problem, a Fast- ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT-TLS method were employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current. The proposed algorithm has been evaluated by computer simulations with many fault scenarios. Study results demonstrate the performance of Fast-ESPRIT allowing fast and high resolution harmonics identification with minimum computation time and less memory cost.
Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality.
Wind power prediction using a nonlinear autoregressive exogenous model netwo...IJECEIAES
The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
An IntelligentMPPT Method For PV Systems Operating Under Real Environmental C...theijes
The sun irradiance (G) and temperature (T) are the two main factors that affect the output power gained from the photovoltaic (PV) DC–DC converter. Therefore, to enhance the performance of the overall system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique, experience difficulty in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). The selection of the membership functions (MFs) and the fuzzy sets (FSs) numbers are crucial in the performance of the FLC based MPPT. Accordingly, this work presents numerous adaptive neuro-fuzzy systems to automatically adjustthe fuzzy logic controller membership functions as an alternative to the trial and error approach, which waste time and effort in MPPT design. For this purpose an adaptive neuro-fuzzy system is developed in MATLAB/Simulink to determine suitable MFs and the FSs for the fuzzy logic controller. The effects of different types of MFs and the FSs are deeply investigated using real data collected from the rooftop PV system. The investigations show that the fuzzy logic controller with a triangular membership function and seven fuzzy setsprovides the best results
Similar to Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system (20)
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
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.
Kosovo has limited renewable energy resources and its power generation sector is based on fossil fuels. Such a situation emphasizes the importance of active research and efficient use of renewable energy potential. According to the analysis of meteorological data for Kosovo, it can be concluded that among the most attractive potential wind power sites are the locations known as Kitka (42° 29' 41" N and 21° 36' 45" E) and Koznica (42° 39′ 32″ N, 21° 22′30″E). The two terrains in which the analysis was carried out are mountain areas, with altitudes of 1142 m (Kitka) and 1230 m (Koznica). the same measuring height, about 84 m above the ground, is obtained for these average wind speeds: Kitka 6,667 m/s and Koznica 6,16 m/s. Since the difference in wind speed is quite large versus a difference in altitude that is not being very large, analyses are made regarding the terrain characteristics including the terrain relief features. In this paper it will be studied how much the roughness of the terrain influences the output energy. Also, that the assumption to be taken the same as to how much they will affect the annual energy produced.
Large-scale grid-tied photovoltaic (PV) station are increasing rapidly. However, this large penetration of PV system creates frequency fluctuation in the grid due to the intermittency of solar irradiance. Therefore, in this paper, a robust droop control mechanism of the battery energy storage system (BESS) is developed in order to damp the frequency fluctuation of the multi-machine grid system due to variable active power injected from the PV panel. The proposed droop control strategy incorporates frequency error signal and dead-band for effective minimization of frequency fluctuation. The BESS system is used to consume/inject an effective amount of active power based upon the frequency oscillation of the grid system. The simulation analysis is carried out using PSCAD/EMTDC software to prove the effectiveness of the proposed droop control-based BESS system. The simulation result implies that the proposed scheme can efficiently curtail the frequency oscillation.
This study investigates experimentally the performance of two-dimensional solar tracking systems with reflector using commercial silicon based photovoltaic module, with open and closed loop control systems. Different reflector materials were also investigated. The experiments were performed at the Hashemite University campus in Zarqa at a latitude of 32⁰, in February and March. Photovoltaic output power and performance were analyzed. It was found that the modified photovoltaic module with mirror reflector generated the highest value of power, while the temperature reached a maximum value of 53 ̊ C. The modified module suggested in this study produced 5% more PV power than the two-dimensional solar tracking systems without reflector and produced 12.5% more PV power than the fixed PV module with 26⁰ tilt angle.
This paper focuses on the modeling and control of a wind energy conversion chain using a permanent magnet synchronous machine. This system behaves a turbine, a generator, DC/DC and DC/AC power converters. These are connected on both sides to the DC bus, where the inverter is followed by a filter which is connected to the grid. In this paper, we have been used two types of controllers. For the stator side converter, we consider the Takagi-Sugeno approach where the parameters of controller have been computed by the theory of linear matrix inequalities. The stability synthesis has been checked using the Lyapunov theory. According to the grid side converter, the proportional integral controller is exploited to keep a constant voltage on the DC bus and control both types of powers. The simulation results demonstrate the robustness of the approach used.
The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (Cr) and modified Rayleigh scale factor (Cm) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R2), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.
This paper deals with an advanced design for a pump powered by solar energyto supply agricultural lands with water and also the maximum power point is used to extract the maximum value of the energy available inside the solar panels and comparing between techniques MPPT such as Incremental conductance, perturb & observe, fractional short current circuit, and fractional open voltage circuit to find the best technique among these. The solar system is designed with main parts: photovoltaic (PV) panel, direct current/direct current (DC/DC) converter, inverter, filter, and in addition, the battery is used to save energy in the event that there is an increased demand for energy and not to provide solar radiation, as well as saving energy in the case of generation more than demand. This work was done using the matrix laboratory (MATLAB) simulink program.
The objective of this paper is to provide an overview of the current state of renewable energy resources in Bangladesh, as well as to examine various forms of renewable energies in order to gain a comprehensive understanding of how to address Bangladesh's power crisis issues in a sustainable manner. Electricity is currently the most useful kind of energy in Bangladesh. It has a substantial influence on a country's socioeconomic standing and living standards. Maintaining a stable source of energy at a cost that is affordable to everyone has been a constant battle for decades. Bangladesh is blessed with a wealth of natural resources. Bangladesh has a huge opportunity to accelerate its economic development while increasing energy access, livelihoods, and health for millions of people in a sustainable way due to the renewable energy system.
When the irradiance distribution over the photovoltaic panels is uniform, the pursuit of the maximum power point is not reached, which has allowed several researchers to use traditional MPPT techniques to solve this problem Among these techniques a PSO algorithm is used to have the maximum global power point (GMPPT) under partial shading. On the other hand, this one is not reliable vis-à-vis the pursuit of the MPPT. Therefore, in this paper we have treated another technique based on a new modified PSO algorithm so that the power can reach its maximum point. The PSO algorithm is based on the heuristic method which guarantees not only the obtaining of MPPT but also the simplicity of control and less expensive of the system. The results are obtained using MATLAB show that the proposed modified PSO algorithm performs better than conventional PSO and is robust to different partial shading models.
A stable operation of wind turbines connected to the grid is an essential requirement to ensure the reliability and stability of the power system. To achieve such operational objective, installing static synchronous compensator static synchronous compensator (STATCOM) as a main compensation device guarantees the voltage stability enhancement of the wind farm connected to distribution network at different operating scenarios. STATCOM either supplies or absorbs reactive power in order to ensure the voltage profile within the standard-margins and to avoid turbine tripping, accordingly. This paper present new study that investigates the most suitable-location to install STATCOM in a distribution system connected wind farm to maintain the voltage-levels within the stability margins. For a large-scale squirrel cage induction generator squirrel-cage induction generator (SCIG-based) wind turbine system, the impact of STATCOM installation was tested in different places and voltage-levels in the distribution system. The proposed method effectiveness in enhancing the voltage profile and balancing the reactive power is validated, the results were repeated for different scenarios of expected contingencies. The voltage profile, power flow, and reactive power balance of the distribution system are observed using MATLAB/Simulink software.
The electrical and environmental parameters of polymer solar cells (PSC) provide important information on their performance. In the present article we study the influence of temperature on the voltage-current (I-V) characteristic at different temperatures from 10 °C to 90 °C, and important parameters like bandgap energy Eg, and the energy conversion efficiency η. The one-diode electrical model, normally used for semiconductor cells, has been tested and validated for the polemeral junction. The PSC used in our study are formed by the poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl C61-butyric acid methyl ester (PCBM). Our technique is based on the combination of two steps; the first use the Least Mean Squares (LMS) method while the second use the Newton-Raphson algorithm. The found results are compared to other recently published works, they show that the developed approach is very accurate. This precision is proved by the minimal values of statistical errors (RMSE) and the good agreement between both the experimental data and the I-V simulated curves. The obtained results show a clear and a monotonic dependence of the cell efficiency on the studied parameters.
The inverter is the principal part of the photovoltaic (PV) systems that assures the direct current/alternating current (DC/AC) conversion (PV array is connected directly to an inverter that converts the DC energy produced by the PV array into AC energy that is directly connected to the electric utility). In this paper, we present a simple method for detecting faults that occurred during the operation of the inverter. These types of faults or faults affect the efficiency and cost-effectiveness of the photovoltaic system, especially the inverter, which is the main component responsible for the conversion. Hence, we have shown first the faults obtained in the case of the short circuit. Second, the open circuit failure is studied. The results demonstrate the efficacy of the proposed method. Good monitoring and detection of faults in the inverter can increase the system's reliability and decrease the undesirable faults that appeared in the PV system. The system behavior is tested under variable parameters and conditions using MATLAB/Simulink.
The electrical distribution network is undergoing tremendous modifications with the introduction of distributed generation technologies which have led to an increase in fault current levels in the distribution network. Fault current limiters have been developed as a promising technology to limit fault current levels in power systems. Though, quite a number of fault current limiters have been developed; the most common are the superconducting fault current limiters, solid-state fault current limiters, and saturated core fault current limiters. These fault current limiters present potential fault current limiting solutions in power systems. Nevertheless, they encounter various challenges hindering their deployment and commercialization. This research aimed at designing a bridge-type nonsuperconducting fault current limiter with a novel topology for distribution network applications. The proposed bridge-type nonsuperconducting fault current limiter was designed and simulated using PSCAD/EMTDC. Simulation results showed the effectiveness of the proposed design in fault current limiting, voltage sag compensation during fault conditions, and its ability not to affect the load voltage and current during normal conditions as well as in suppressing the source powers during fault conditions. Simulation results also showed very minimal power loss by the fault current limiter during normal conditions.
This paper provides a new approach to reducing high-order harmonics in 400 Hz inverter using a three-level neutral-point clamped (NPC) converter. A voltage control loop using the harmonic compensation combined with NPC clamping diode control technology. The capacitor voltage imbalance also causes harmonics in the output voltage. For 400 Hz inverter, maintain a balanced voltage between the two input (direct current) (DC) capacitors is difficult because the pulse width modulation (PWM) modulation frequency ratio is low compared to the frequency of the output voltage. A method of determining the current flowing into the capacitor to control the voltage on the two balanced capacitors to ensure fast response reversal is also given in this paper. The combination of a high-harmonic resonator controller and a neutral-point voltage controller working together on the 400 Hz NPC inverter structure is given in this paper.
Direct current (DC) electronic load is a useful equipment for testing the electrical system. It can emulate various load at a high rating. The electronic load requires a power converter to operate and a linear regulator is a common option. Nonetheless, it is hard to control due to the temperature variation. This paper proposed a DC electronic load using the boost converter. The proposed electronic load operates in the continuous current mode and control using the integral controller. The electronic load using the boost converter is compared with the electronic load using the linear regulator. The results show that the boost converter able to operate as an electronic load with an error lower than 0.5% and response time lower than 13 ms.
More from International Journal of Power Electronics and Drive Systems (20)
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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
2. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Perfomance comparison of artificial intelligence techniques in short term… (Muhammad Murtadha Othman)
2149
overfitting the output results, the run time process is fast and efficient when handling a large dataset thus gives
it superior predictive performance.
This paper presents the ANN and RF methods that used to perform short-term PV output current
forecasting for the next 24-hours. There is not much research that has been done regarding to the PV output
current forecasting using RF. The input data used for this method is current, irradiance, hours and temperature
will pass through the filtration process using the wavelet decomposition to eliminate the noises in each data.
Then, the multiple time lags is used due to its capabilities to identify the pattern and behavior of filtered data
while improving it for accurate estimation in the next 24 hours of PV output current forecasting [16-20]. The
case study uses PV output current, temperature, irradiance and hours in 2015 with the total of 7460 hourly data
obtained from the Green Energy Research Center (GERC), University Technology Mara Shah Alam, Malaysia.
The robustness of both models in forecasting are compared by referring to the mean square error (MSE), mean
absolute percentage error (MAPE) and regression between the forecasted and actual (targeted) values.
2. RESEARCH METHOD
This segment will explain the concept of feature extraction or data preparation for the ANN and RF
models used in the PV output current forecasting [21-25]. The structure used for this process is shown in Figure
1 wherein the STPCF process begins from the original data selection. In this case, the hourly PV output current,
temperature, irradiance and hours are selected as the input data. After data selection, the data preparation for
the input and target data is performed by using the wavelet decomposition and multiple time lags technique.
Subsequently, the forecasting models for ANN and RF are designed. Finally, the training and testing procedure
is performed to obtain the forecasting outcome from ANN and RF.
Figure 1. Block diagram of PV output current forecasting modeling
2.1. Input data of chronological parameter
The information of data used in forecasting is acquired from the Green Energy Research Centre
(GERC) of UiTM Shah Alam, Malaysia. The data is obtained in the form of MATLAB software. The data is
obtained consisting with five parameters in 2015. In the GERC laboratory, this information is collected by data
logger for every 5 minutes. This data will be analyzed and the hour, irradiance, temperature, power and current
with maximum parameter value will be used in forecasting as shown in Table 1.
Table 1. Information for each parameter
Parameter Maximum Value Unit
Hour 24 Hour
Irradiance 1320 W/m²
Temperature 47.9 °C
Power 26847.61 Watt
Current 9.98 Ampere
The data preparation for ANN and RF models is shown in Figure 2 and its procedure is elaborated as
follows.
Figure 2. Block diagram for data preparation
Raw data of
hours,
temperature,
irradiance,
current and
power
Wavelet
decompositio
n
Data
normalized
Multiple
Time Lags
Data
Arrangement
3. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 10, No. 3, Sep 2019 : 2148 – 2156
2150
- Collect the raw data consisting with 7460 hourly information of hour, irradiance, temperature, power and
current.
- Perform the filtering process of input data by using the wavelet decomposition to reduce any noise inside it.
- Normalize all of the filtered data by dividing with its maximum value in order to reduce data redundancy
within the range of 0 and 1.
- Perform the multiple time lags to improve the data to determine the movement pattern of every input data
required by the ANN and RF techniques.
- Use the multiple time lags to estimate the future variable and the lagged (past period) variable that will
evolved in the future [9, 9, 9, 9, 9]. The input data improved by the multiple time lagsable to determine the
movement pattern of data in the neural network based on (1). The total number of time interval lagging is
K=24 hours.
Lagk = Zt – Zt-k (1)
where,
t : time interval.
k : time interval lags 1, 2, 3…K.
K : total number of time interval lagging.
In (1), the total number of time interval lagging used is K=24 where the value of K is stated to be
equivalent to the time interval in the forecasted variable. The value of K is fixed to 24 for forecasting the next
24 hours of PV output current. The input data is in the form of k-by-t matrix where each column will be used
to forecast PV output current for the next 24-hours. The first column of training data, Lagk is used to forecast
the target data of X48. The input data arrangement for training and target data is shown in Figure 3.
Figure 3. The input data arrangement for training data and target data for ANN and RF in Lag 24
Two sets of data which is training data and target data is created after all data have been converted
into multiple time lags. The arrangement of training data for each line is a combination of hour, temperature,
irradiance and current. The last part of current data will be used as target data. The training and target data
formed will be used for forecasting using ANN and RF methods. The chronological arrangement of input data
is shown in Table 2.
Table 2. Chronological arrangement of input data
Input data
Parameter for each line after Lag 24
1 to 23 hour
24 to 46 temperature
47 to 69 irradiance
70 to 92 current
Target Data
4. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Perfomance comparison of artificial intelligence techniques in short term… (Muhammad Murtadha Othman)
2151
Current
2.2. Artificial neural network (ANN)
The Artificial Neural Network (ANN) is an alternative method that has been efficiently carried out in
this paper as it is also suited to tackle solar energy uncertainty issues. The ANN is using the Levenberg-
Marquardt technique to forecast the PV output current for the next 24 hours [4].
In this case study, the ANN model for forecasting the PV output current is consisting of one input
layer, two hidden layers, and one output layer. The output layer of ANN is consisting of one neuron which will
provides the predicted PV output current for the next 24 hours. The Levenberg-Marquardt technique is used in
the ANN model as back propagation algorithm for optimization of the data during the training process. This
technique is commonly used in forecasting the training set of ANN due to its algorithm that compromise
between the accuracy and stability of prediction to achieve the steepest method for measuring minimal errors.
The ANN model for forecasting PV output current is shown in Figure 4 and the ANN procedure is
explained below.
- Divide input data into three sets of training, testing and validating for the multiple time lags of K=24 hours.
- In the training process, the synapses minimize the error between the actual output and the targeted output by
regulating the learning rate and momentum.
- Select the number of hidden layers is based on the fact of one hidden layer is sufficient to estimate any
function. Therefore, two hidden layers is used in this ANN models that will provide more precise results with
minimum RMS error in forecasting the next 24 hours of PV output current.
- Repeat the error minimization process until the optimization process in forecasting is converged yielding to
the smallest error in its output. Then, the training procedure is terminated once the minimum error becomes
plateau for several iterations of optimization process involved in the ANN.
- Identify the strength of ANN in producing the correct STPCF results that can be proven by conducting the
testing then validation processes by using different set of input data.
Figure 4. Block diagram for ANN model
2.3. Random forest (RF)
Random Forest is a model comprising with two significant components of tree bagging and random
decision trees [6]. The TreeBagger defined as B is containing with the number of trees (NTrees) with the X and
Y as the ensemble function that been used for creating a decision tree. The decision tree uses the input function
X to predict the target response Y. The procedure of Random Forest is explained below.
- Perform bootstrap samples, N randomly drawn from the training data of RF model, to create a regression
trees for each sample. The bootstrap sample is having the same size as the original training data.
- Perform the bagging technique that divides the bootstrap sample into two sets of data which is two-third is
for the In-Bag while the remaining data is for the Out-Of-Bag (OOB).
- Use the InBag to create a forest wherein the tree growth technique will produce the best leaves. . The OOB
data is used to run the unbiased prediction error as trees are added into the forest during tree growth phase
using the InBag data. The primary role of OOB data in tree growth technique is to compare its estimation
with the predicted values obtained from the InBag to find the best leaves with minimal error rate from
every tree.
- Halt the growth of the tree once the final node of best leaf in every tree is obtained. Upon finishing the final
nodes, the prediction value from the final node of best leaf is collected from every tree and the average
prediction is calculated from the final node leaf of all trees. Figure 5 shows the structure of RF.
5. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 10, No. 3, Sep 2019 : 2148 – 2156
2152
Figure 5. Structure of RF algorithm
3. RESULTS AND ANALYSIS
This section discussed on the STPCF results determined by using the ANN and RF models. The data
of hourly solar irradiance, temperature, hour, and current obtained from Green Energy Research (GERC)
University Technology Mara Shah Alam, Malaysia is used for the case study of STPCF. The input data
undergoes the wavelet decomposition to eliminate the noise inside the data and then the multiple time lags of
K=24-hours is applied to the filtered data. The data size used in ANN and RF procedure is 17520 columns.
The data is divided into three sets wherein the data size for training is 5785 columns, testing data is 720 columns
and validation data is 720 columns.
3.1. Artificial neural network (ANN)
The input data of ANN is the combination of multiple time lags of hour, temperature, irradiance and
current. Training and testing procedures of ANN are performed where the input data having the multiple time
lags of K = 24 hours. In the ANN model, the number of neurons for the first hidden layer is 20 and second
hidden layer is 10. While, learning rate and momentum is 0.3 respectively. The numbers for first hidden layer,
second hidden layer, learning rate and momentum are selected by performing sensitivity analysis where the
selected values of learning rate and is referring to the minimum RMSE value of output.
Table 3. Results of ANN considering all the best parameters
ANN Output for K=24
Training sets 5785
Testing sets 720
Number of neuron in 1st
hidden layer 20
Number of neuron in 2nd
hidden layer 10
Number of output 1
Learning rate 0.3
Momentum 0.3
Training function Levenberg- Marquardt
Training RMSE 0.5270
Testing RMSE 0.5301
Regression 0.99342
Minimum MAPE 0.2832
Maximum MAPE 13.2377
Mean MAPE 4.4217
6. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Perfomance comparison of artificial intelligence techniques in short term… (Muhammad Murtadha Othman)
2153
Figures 6(a) and 6(b) represent the result of forecasted PV output current versus actual targeted values,
and the regression of output results obtained during the testing procedure of ANN, respectively. In Figure 6(a),
the actual targeted output is in blue colour and the forecasted PV output current is in red colour. The forecasted
pattern of hourly PV output current is almost the same with the pattern of actual targeted values at certain
hours. However, there is inconsistency with several large error in the variation between the forecasted and
targeted PV output current for the next 24 hours.
(a) (b)
Figure 6. STPCF for the next 24-hours using ANN for the (a) forecasted PV output current versus actual
targeted values, (b) regression of forecasted versus actual PV output current
3.2. Random forest (RF)
The multiple time lags of hour, temperature, irradiance and current are used as the input data of RF.
The training, testing and validating processes of RF are conducted using the input data with multiple time lags
of K=24 hours. The RF is conduct at three different cases of 1, 5 and 10 number of trees (Ntrees) and every
tree consisting of 5 leaves. The selection for the number of trees is based on the fact that single tree is sufficient
to estimate any function. Therefore, two trees will provide more precision in determining the minimum error.
The mean square error (MSE) is obtained from the training procedure. However, in the testing procedure, the
MSE is automatically compared its output with the targeted data at each leaf in every tree. These comparisons
are performed until the finest trees expansion is achieved giving the minimum average of RMS error for the
final node leaf of all trees. The number of trees chosen for the sensitivity analysis to determine the best
prediction of PV output current with minimum RMSE values RF is shown in Table 4.
Table 4. Results of RF considering all the best parameters
RF Output for K=24
Training sets 5785
Testing sets 720
Validation sets 720
Training function Bootstrapping
Output Function Regression
Number of trees 1 5 10
Number of leaves 5 5 5
Training RMSE 0.8725 0.8753 0.8756
Testing RMSE 0.0476 0.0089 0.0078
Regression 0.99967 0.99993 0.99994
Minimum MAPE 0.0061 0.0016 0.0111
Maximum MAPE 2.0735 0.6109 0.7953
Mean MAPE 0.2731 0.2286 0.2000
7. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 10, No. 3, Sep 2019 : 2148 – 2156
2154
Figures 7(a) and 7(b) represent the comparative results and regression of forecasted PV output current
versus actual targeted values obtained from the testing procedure of ANN, respectively. It can be observed that
the forecasted hourly PV output current provide a very similar variation with minimum error as compared to
the actual targeted values pattern.
(a)
(b)
Figure 7. STPCF for the next 24-hours using RF for the (a) forecasted PV output current versus actual
targeted values, (b) regression of forecasted versus actual PV output current
3.3. MAPE comparison between the performance of ANN and RF
Tables 3 and 4 have shown the results of ANN and RF, respectively. The output result is obtained by
considering to multiple time lags K = 24 hours during testing procedure for both techniques. The testing
procedure for both techniques with multiple time lags is further investigated by comparing the MAPE results
of PV output current.
By referring to the MAPE results of ANN and RF in Table 5, it can be observed that the ANN model
produces a higher MAPE value of 5.0836%, in contrast with the MAPE of 0.0579% determined by the RF in
day two. It is perspicuous in Table 5 that the RF provides most accurate prediction with the minimum average
MAPE results in forecasting the PV output current as compared to the ANN. It is obvious that bagging
technique improves the training and testing processes of RF in obtaining the best results with minimum error
in forecasting. This implies that the ANN is far more complicated than the RF in terms of interpreting and
understanding the weight, easy to over-fit the model and unpredicted in its performance.
8. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Perfomance comparison of artificial intelligence techniques in short term… (Muhammad Murtadha Othman)
2155
Table 5. MAPE of forecasted PV output current for the next 24-hours obtained from the ANN and RF
Day
ANN RF
K = 24
MAPE (%) MAPE (%)
1 5.9144 0.1184
2 5.0836 0.0579
3 3.0745 0.3008
4 2.9441 0.1622
5 2.4173 0.1184
6 6.0031 0.1219
7 3.3752 0.7556
8 4.7268 0.1420
9 2.5261 0.0864
10 6.9916 0.2823
11 8.9894 0.7953
12 2.2936 0.0190
13 6.2087 0.0574
14 1.7835 0.2749
15 4.5219 0.3175
16 1.0799 0.0118
17 6.7759 0.2154
18 13.2377 0.1462
19 11.4929 0.0111
20 0.3173 0.0243
21 0.9236 0.2045
22 0.2832 0.0320
23 3.3776 0.0203
24 4.4665 0.4610
25 4.4561 0.0198
26 5.9024 0.1180
27 5.0569 0.3915
28 3.0791 0.3008
29 2.9465 0.2508
30 2.4028 0.1835
Average
MAPE
4.4217 0.2000
4. CONCLUSION
The application of artificial neural network (ANN) and random forest (RF) with wavelet denoising
and multiple time lags K=24 in performing short term photovoltaic current forecasting (STPCF) has been
discussed elaborately in this paper. The results shown proved that the models proposed for the case study have
the benefit of providing accurate result of STPCF. However, the RF method shown the important of choosing
the accurate number of tree and leaf to be used as it will affect the performance of RF. The result shown that
the RF method able to forecast the PV output current for the next 24 hours and provide more accurate results
of STPCF with minimum error compared to ANN.
ACKNOWLEDGEMENT
This research was supported by the Long-Term Research Grant (LRGS), Ministry of Education
Malaysia for the program titled "Decarbonisation of Grid with an Optimal Controller and Energy Management
for Energy Storage System in Microgrid Applications" with project code 600-IRMI/LRGS 5/3 (001/2019). The
authors would also like to acknowledge The Institute of Research Management & Innovation (IRMI),
Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia for the facilities provided to support on
this research.
REFERENCES
[1] M. Hosenuzzaman, N. A. Rahim, J. Selvaraj, M. Hasanuzzaman, A. B. M. A. Malek, and A. Nahar, “Global
prospects, progress, policies, and environmental impact of solar photovoltaic power generation,” Renew. Sustain.
Energy Rev., vol. 41, pp. 284–297, 2015.
[2] J. F. M. Pessanha and N. Leon, “Forecasting long-term electricity demand in the residential sector,” Procedia
Comput. Sci., vol. 55, no. Itqm, pp. 529–538, 2015.
[3] G. R. T. Esteves, B. Q. Bastos, F. L. Cyrino, R. F. Calili, and R. C. Souza, “Long term electricity forecast: A
systematic review,” Procedia Comput. Sci., vol. 55, no. Itqm, pp. 549–558, 2015.
[4] M. M. Othman, and I. Musirin, "Optimal sizing and operational strategy of hybrid renewable energy system using
9. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 10, No. 3, Sep 2019 : 2148 – 2156
2156
homer," In IEEE 2010 4th International Power Engineering and Optimization Conference (PEOCO), 2010.
[5] A. Qais, M. M. Othman, N. Khamis, and I. Musirin, "Optimal sizing and operational strategy of PV and micro-
hydro," In 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO), pp. 714-717,
2013.
[6] M. Ghazvini, A. Abbaspour-Tehrani-Fard, M. Fotuhi-Firuzabad, and M. M. Othman, "Optimizing size and operation
of hybrid energy systems," In 2013 IEEE 7th International Power Engineering and Optimization Conference
(PEOCO), pp. 489-494, 2013.
[7] S. M. H. W. Dawi, M. M. Othman, I. Musirin, A. A. M. Kamaruzaman, A. M. Arriffin, and N. A. Salim, "Gamma
Stirling Engine for a Small Design of Renewable Resource Model," Indonesian Journal of Electrical Engineering
and Computer Science, vol. 8, no. 2, pp. 350-359, 2017.
[8] F. Zahari, M. M. Othman, I. Musirin, A. A. M. Kamaruzaman, N. A. Salim, and B. N. Sheikh Rahimullah, "Design
of a small renewable resource model based on the stirling engine with alpha and beta configurations," Indonesian
Journal of Electrical Engineering and Computer Science, vol. 8, no. 2, pp. 360-367, 2017.
[9] F. Wang, Z. Mi, S. Su, and H. Zhao, “Short-term solar irradiance forecasting model based on artificial neural network
using statistical feature parameters,” Energies, vol. 5, no. 5, pp. 1355–1370, 2012.
[10] C. Voyant, G. Notton, S. Kalogirou, M.-L. Nivet, C. Paoli, F. Motte, and A. Fouilloy, "Machine learning methods
for solar radiation forecasting: A review," Renewable Energy, vol.105, pp. 569-582, 2017.
[11] M. Ding, L. Wang, and R. Bi, “An ANN-based approach for forecasting the power output of photovoltaic system,”
Procedia Environ. Sci., vol. 11, no. PART C, pp. 1308–1315, 2011.
[12] P. Mandal, S. T. S. Madhira, A. Ul haque, J. Meng, and R. L. Pineda, “Forecasting power output of solar photovoltaic
system using wavelet transform and artificial intelligence techniques,” Procedia Comput. Sci., vol. 12, no. 915, pp.
332–337, 2012.
[13] I. A. Ibrahim, T. Khatib, A. Mohamed, and W. Elmenreich, “Modeling of the output current of a photovoltaic grid-
connected system using random forests technique,” Energy Explor. Exploit., vol. 36, no. 1, pp. 132–148, 2018.
[14] L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
[15] M. Kayri, I. Kayri, and M. T. Gencoglu, “The performance comparison of Multiple Linear Regression, Random
Forest and Artificial Neural Network by using photovoltaic and atmospheric data,” 2017 14th Int. Conf. Eng. Mod.
Electr. Syst. EMES 2017, pp. 1–4, 2017.
[16] M. H. H. Harun, M. M. Othman, and I. Musirin, “Short term load forecasting (STLF) using artificial neural network
based multiple lags of time series,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics), vol. 5507 LNCS, no. PART 2, pp. 445–446, 2009.
[17] M. H. H. Harun, M. M. Othman, and I. Musirin, "Short term load forecasting (STLF) using artificial neural network
based multiple lags and stationary time series," In 2010 4th International Power Engineering and Optimization
Conference (PEOCO), pp. 363-370, 2010.
[18] M. M. Othman, M. H. H. Harun, and I. Musirin, "Forecasting short term electric load based on stationary output of
artificial neural network considering sequential process of feature extraction methods," In 2012 IEEE International
Power Engineering and Optimization Conference Melaka, Malaysia, pp. 485-489, 2012.
[19] M. M. Othman, M. H. H. Harun, N. A. Salim, and M. L. Othman, "Sequential process of feature extraction methods
for artificial neural network in short term load forecasting," ARPN Journal of Engineering and Applied Sciences, vol.
10, no. 19, pp. 8830-8838, 2015.
[20] M. M. Othman, M. H. H. Harun, and I. Musirin, "Short term load forecasting using artificial neural network with
feature extraction method and stationary output," In 2012 IEEE International Power Engineering and Optimization
Conference Melaka, Malaysia, pp. 480-484, 2012.
[21] J. S. Armstrong, “Illusions in Regression Analysis @ upenn.academia.edu,” vol. 2012, no. 3, pp. 961–967, 2012.
[22] P.-H. Chiang, S. P. V. Chiluvuri, S. Dey, and T. Q. Nguyen, “Forecasting of Solar Photovoltaic System Power
Generation Using Wavelet Decomposition and Bias-Compensated Random Forest,” 2017 Ninth Annu. IEEE Green
Technol. Conf., pp. 260–266, 2017.
[23] V. Lo Brano, G. Ciulla, and M. Di Falco, “Artificial Neural Networks to Predict the Power Output of a PV Panel,”
Int. J. Photoenergy, vol. 2014, p. 12, 2014.
[24] R. Perez, S. Kivalov, J. Schlemmer, K. Hemker J. D. Renné, and T. E. Hoff, “Validation of Short and Medium Term
Operational Solar Radiation Forecasts in the U.S.,” no. 2010, 2009.
[25] P. Bacher, H. Madsen, and H. A. Nielsen, “Online short-term solar power forecasting,” Sol. Energy, vol. 83, no. 10,
pp. 1772–1783, 2009.