International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Design of Linear Plasma Position Controllers with Intelligent Feedback System...IJECEIAES
In order to increase the performance of Aditya tokamak, it is necessary to determine the feedback coil current for positioning the plasma within the magnetic chamber. In this paper, transfer functions are obtained for the plasma position prediction system. Four different feedback controllers are developed to improve the performance of the prediction system. From the analysis, Neural Network controller does not have overshoot while the PID controller has lesser settling time than the other two controllers.
Performance enhancement of maximum power point tracking for grid-connected ph...TELKOMNIKA JOURNAL
This paper presents a new variant of smart adaptive algorithm of Maximum Power Point Tracking (MPPT) in the photovoltaic (PV) system. The algorithm was adopted from Modified Perturb and Observe (MP&O). The smart adaptive MPPT is used to search Maximum Power Point (MPP) of the PV system under various irradiance changes. This algorithm incorporates information of current change (ΔI), maximum operating point margin and dynamic perturbation step to prevent MPPT diverging away from the MPP and minimize the steady state oscillation. The smart adaptive MPPT algorithm performance is compared with the dI-P&O and conventional P&O to prove its effectiveness. The comparison is performed under the various gradient of irradiance change. It was found that, for all the tests, the smart adaptive algorithm scheme improve the tracking efficiency under various gradients of irradiance changes and increase the efficiency of extraction power from PV system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Design of Linear Plasma Position Controllers with Intelligent Feedback System...IJECEIAES
In order to increase the performance of Aditya tokamak, it is necessary to determine the feedback coil current for positioning the plasma within the magnetic chamber. In this paper, transfer functions are obtained for the plasma position prediction system. Four different feedback controllers are developed to improve the performance of the prediction system. From the analysis, Neural Network controller does not have overshoot while the PID controller has lesser settling time than the other two controllers.
Performance enhancement of maximum power point tracking for grid-connected ph...TELKOMNIKA JOURNAL
This paper presents a new variant of smart adaptive algorithm of Maximum Power Point Tracking (MPPT) in the photovoltaic (PV) system. The algorithm was adopted from Modified Perturb and Observe (MP&O). The smart adaptive MPPT is used to search Maximum Power Point (MPP) of the PV system under various irradiance changes. This algorithm incorporates information of current change (ΔI), maximum operating point margin and dynamic perturbation step to prevent MPPT diverging away from the MPP and minimize the steady state oscillation. The smart adaptive MPPT algorithm performance is compared with the dI-P&O and conventional P&O to prove its effectiveness. The comparison is performed under the various gradient of irradiance change. It was found that, for all the tests, the smart adaptive algorithm scheme improve the tracking efficiency under various gradients of irradiance changes and increase the efficiency of extraction power from PV system.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
HEURISTIC BASED OPTIMAL PMU ROUTING IN KPTCL POWER GRIDIAEME Publication
Power system monitoring is an important process in an efficient smart grid. The control centers used in smart grid requires restructuring. State measurements rather than state estimationare pre-requisite for the modern control center. The Phasor Measurement Unit (PMU) measures the synchronized voltage and current parameters. Placement of minimum number of PMUs in a bus system such that the wholes system becomes observable is considered as Optimal PMU Placement (OPP) problem. In this paper, Hybrid Distance Optimization (HDO) algorithm is proposed to reduce the number of PMUs for complete observability along with the minimum length of fiber optic cable required for interconnecting the PMU nodes
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.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
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.
VOLTAGE STABILITY IN NIGERIA 330KV INTEGRATED 52 BUS POWER NETWORK USING PATT...Onyebuchi nosiri
ABSTRACT Detecting the voltage instability in advance enables remedial actions and preventive measures to cushion the effect of the oncoming voltage collapse phenomenon in power systems. This was achieved by implementing Pattern Recognition Techniques (PRTs) in conjunction with Power System Simulator for Engineering (PSSE) program. It was then deployed in Nigeria 330KV Integrated 52 bus power system to actualize Regularized Least Squares Classification (RLSC) and Classification and Regression Trees (CART) heuristic methods. The methods were deployed for separating voltage stability and unstable cases that resulted under system contingencies and fault conditions. Dynamic simulation, system voltage stability and unstable/instability cases results, and the channel outputs of these voltage cases against time were realized.
VOLTAGE STABILITY IN NIGERIA 330KV INTEGRATED 52 BUS POWER NETWORK USING PATT...Onyebuchi nosiri
ABSTRACT Detecting the voltage instability in advance enables remedial actions and preventive measures to cushion the effect of the oncoming voltage collapse phenomenon in power systems. This was achieved by implementing Pattern Recognition Techniques (PRTs) in conjunction with Power System Simulator for Engineering (PSSE) program. It was then deployed in Nigeria 330KV Integrated 52 bus power system to actualize Regularized Least Squares Classification (RLSC) and Classification and Regression Trees (CART) heuristic methods. The methods were deployed for separating voltage stability and unstable cases that resulted under system contingencies and fault conditions. Dynamic simulation, system voltage stability and unstable/instability cases results, and the channel outputs of these voltage cases against time were realized.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed specifically for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robot’s responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
A Novel Technique for Tuning PI-controller in Switched Reluctance Motor Drive...IJECEIAES
This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Partial Shading Detection and MPPT Controller for Total Cross Tied Photovolta...IDES Editor
This paper present Maximum Power Point Tracking
(MPPT) controller for solving partial shading problems in
photovoltaic (PV) systems. It is well-known that partial shading
is often encountered in PV system issue with many
consequences. In this research, PV array is connected using
TCT (total cross-tied) configuration including sensors to
measure voltage and currents. The sensors provide inputs for
MPPT controller in order to achieve optimum output power.
The Adaptive Neuro Fuzzy Inference System (ANFIS) is
utilized in this paper as the controller methods. Then, the
output of MPPT controller is the optimum power duty cycle
(α) to drive the performance DC-DC converter. The simulation
shows that the proposed MPPT controller can provide PV
voltage (VMPP) nearly to the maximum power point voltage.
The accuracy of our proposed method is measured by
performance index defined as Mean Absolute Percentage Error
(MAPE). In addition, the main purpose of this work is to
present a new method for detecting partial condition of
photovoltaic TCT configuration using only 3 sensors. Thus,
this method can streamline the time and reduce operating
costs.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
HEURISTIC BASED OPTIMAL PMU ROUTING IN KPTCL POWER GRIDIAEME Publication
Power system monitoring is an important process in an efficient smart grid. The control centers used in smart grid requires restructuring. State measurements rather than state estimationare pre-requisite for the modern control center. The Phasor Measurement Unit (PMU) measures the synchronized voltage and current parameters. Placement of minimum number of PMUs in a bus system such that the wholes system becomes observable is considered as Optimal PMU Placement (OPP) problem. In this paper, Hybrid Distance Optimization (HDO) algorithm is proposed to reduce the number of PMUs for complete observability along with the minimum length of fiber optic cable required for interconnecting the PMU nodes
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.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
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.
VOLTAGE STABILITY IN NIGERIA 330KV INTEGRATED 52 BUS POWER NETWORK USING PATT...Onyebuchi nosiri
ABSTRACT Detecting the voltage instability in advance enables remedial actions and preventive measures to cushion the effect of the oncoming voltage collapse phenomenon in power systems. This was achieved by implementing Pattern Recognition Techniques (PRTs) in conjunction with Power System Simulator for Engineering (PSSE) program. It was then deployed in Nigeria 330KV Integrated 52 bus power system to actualize Regularized Least Squares Classification (RLSC) and Classification and Regression Trees (CART) heuristic methods. The methods were deployed for separating voltage stability and unstable cases that resulted under system contingencies and fault conditions. Dynamic simulation, system voltage stability and unstable/instability cases results, and the channel outputs of these voltage cases against time were realized.
VOLTAGE STABILITY IN NIGERIA 330KV INTEGRATED 52 BUS POWER NETWORK USING PATT...Onyebuchi nosiri
ABSTRACT Detecting the voltage instability in advance enables remedial actions and preventive measures to cushion the effect of the oncoming voltage collapse phenomenon in power systems. This was achieved by implementing Pattern Recognition Techniques (PRTs) in conjunction with Power System Simulator for Engineering (PSSE) program. It was then deployed in Nigeria 330KV Integrated 52 bus power system to actualize Regularized Least Squares Classification (RLSC) and Classification and Regression Trees (CART) heuristic methods. The methods were deployed for separating voltage stability and unstable cases that resulted under system contingencies and fault conditions. Dynamic simulation, system voltage stability and unstable/instability cases results, and the channel outputs of these voltage cases against time were realized.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed specifically for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robot’s responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
A Novel Technique for Tuning PI-controller in Switched Reluctance Motor Drive...IJECEIAES
This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Partial Shading Detection and MPPT Controller for Total Cross Tied Photovolta...IDES Editor
This paper present Maximum Power Point Tracking
(MPPT) controller for solving partial shading problems in
photovoltaic (PV) systems. It is well-known that partial shading
is often encountered in PV system issue with many
consequences. In this research, PV array is connected using
TCT (total cross-tied) configuration including sensors to
measure voltage and currents. The sensors provide inputs for
MPPT controller in order to achieve optimum output power.
The Adaptive Neuro Fuzzy Inference System (ANFIS) is
utilized in this paper as the controller methods. Then, the
output of MPPT controller is the optimum power duty cycle
(α) to drive the performance DC-DC converter. The simulation
shows that the proposed MPPT controller can provide PV
voltage (VMPP) nearly to the maximum power point voltage.
The accuracy of our proposed method is measured by
performance index defined as Mean Absolute Percentage Error
(MAPE). In addition, the main purpose of this work is to
present a new method for detecting partial condition of
photovoltaic TCT configuration using only 3 sensors. Thus,
this method can streamline the time and reduce operating
costs.
Design Of Charge Controller Using MPPT AlgorithmIJRES Journal
Recently non-conventional sources are in huge demand than the conventional sources of energy. Solar energy, though it is in great demand but it has low efficiency. So, to increase the efficiency of the system, we need to find the exact MPP. For this we employ a tracker called MPPT. The main aim will be to track the maximum power from the photovoltaic and feed the extracted power to the load via buck-boost converter. The purpose of this converter is to maintain the required voltage magnitude necessary for the load. In this paper, I have used P&O Algorithm to get the maximum power point and for efficiently designing the charge controller.
Design Document - Remote Solar Monitoring and Bateery Optimzation SystemMamoon Ismail Khalid
• Developed a Battery & Solar Photo Voltaic Monitoring System as part of Senior Year undergraduate project
• Ranked among Top 5 projects departmentally
• Through the use of real time data analysis (battery current,voltage, total load, battery capacity) via MATLAB /C++ script, we were able to increase the battery usage life of a standard 100 W battery by up to 100%, which would almost half the costs of any renewable energy system.
Adaptive Neuro-Fuzzy Inference System based Fractal Image CompressionIDES Editor
This paper presents an Adaptive Neuro-Fuzzy
Inference System (ANFIS) model for fractal image
compression. One of the image compression techniques in
the spatial domain is Fractal Image Compression (FIC)
but the main drawback of FIC using traditional
exhaustive search is that it involves more computational
time due to global search. In order to improve the
computational time and compression ratio, artificial
intelligence technique like ANFIS has been used. Feature
extraction reduces the dimensionality of the problem and
enables the ANFIS network to be trained on an image
separate from the test image thus reducing the
computational time. Lowering the dimensionality of the
problem reduces the computations required during the
search. The main advantage of ANFIS network is that it
can adapt itself from the training data and produce a
fuzzy inference system. The network adapts itself
according to the distribution of feature space observed
during training. Computer simulations reveal that the
network has been properly trained and the fuzzy system
thus evolved, classifies the domains correctly with
minimum deviation which helps in encoding the image
using FIC.
This Paper mainly deals with the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) in Pulse Width Modulation control of Single Ended Primary Inductor Converter (SEPIC). Generally PID, Fuzzy techniques are being used to control DC – DC converter. This paper presents a ANFIS controller based SEPIC converter for maximum power point tracking (MPPT) operation of a photovoltaic (PV) system. The ANFIS controller for the SEPIC MPPT scheme shows a high precision in current transition and keeps the voltage without any changes represented in small steady state error and small overshoot. The proposed scheme ensures optimal use of photovoltaic (PV) array, wind turbine and proves its efficacy in variable load conditions, unity and lagging power factor at the inverter output (load) side. The performance of the proposed ANFIS based MPPT operation of SEPIC converter is compared to those of the conventional PID and Fuzzy based SEPIC converter. The results show that the proposed ANFIS based MPPT scheme for SEPIC can transfer power to about 20 percent (approx) more than conventional system.
The solar energy is converted to electrical energy by photo-voltaic cells. This energy is stored in batteries during day time for utilizing the same during night time. A charge controller, or charge regulator is basically a voltage and/or current regulator to keep batteries from overcharging. It regulates the voltage and current coming from the solar panels going to the battery.
A brief overview of PV market globally and regionally is presented and how it has disrupted the current network business model. Energy Storage has become a necessity as penetration of PV in the current network increases and created challenging ramping issues as the daily load curves have changed to what is now popularly called “Duck” curves. The value of energy storage in the network is presented, clearly demonstrating that maximum value is realised at end users, commercial and residential. Battery storage is one of the most practical option. Commercial battery technologies are presented, followed by selected case studies.
Major Project report "MPPT BASED BATTERY CHARGING USING SOLAR ENERGY" (or) so...ViJay ChouDhary
A Major Project Report on
MPPT BASED BATTERY CHARGING USING SOLAR
ENERGY
” in fulfillment of the requirement for
the award of the degree of Bachelor of Technology in Electrical Engineering
submitted in the Department of Electrical Engineering, MANIT, Bhopal
The .ppt provides slides representing :
1. How Marine pollution changed the beauty of world.
\n
2. Causes & Effects of :
a.Toxic Ocean Pollutants.
b.Marine Garbage.
c.Sewage Disposal in Ocean.
d.Non-Point Pollutants.
3.Origin.
4.Conventions to prevent it.
5.Various prevention measures:
a.Green infrastructure approach.
b.Septic tank.
c.Dissolved air flotation.
d.Urban runoff.
6.Conclusion.
Regards to all.
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
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.
In this paper, a maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is achieved based on fuzzy logic controller (FLC) and compared with an anfis (neuro-fuzzy) based mppt controller, this method allies the abilities of artificial neural networks in learning and the power of fuzzy logic to handle imprecise data. Both methods are simulated using matlab/ simulink. The choise of power variation and the current variation as inputs of the proposed controllersreducesthe calculation. Both FLC and ANFIS based MPPTare tested in terms of steady state performance and the pv system dynamic.
This paper presents a maximum power point (MPP) tracking method based on a hybrid combination between the fuzzy logic controller (FLC) and the conventional Perturb-and-Observe (P&O) method. The proposed algorithm utilizes the FLC to initialize P&O algorithm with an initial duty cycle. MATLAB/Simulink models consisting of, the photovoltaic system, boost converter and controllers, are built to evaluate the performance of the proposed algorithm. To accurately illustrate the performance of the proposed algorithm, comparisons with standalone FLC and P&O are carried out. The performance of the proposed algorithm is investigated difficult weather conditions including sudden changes and partial shading. The results showed that the proposed algorithm successfully reaches MPP in all scenarios.
ANFIS used as a Maximum Power Point Tracking Algorithmfor a Photovoltaic Syst...IJECEIAES
Photovoltaic (PV) modules play an important role in modern distribution networks; however, from the beginning, PV modules have mostly been used in order to produce clean, green energy and to make a profit. Working effectively during the day, PV systems tend to achieve a maximum power point accomplished by inverters with built-in Maximum Power Point Tracking (MPPT) algorithms. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS), as a method for predicting an MPP based on data on solar exposure and the surrounding temperature. The advantages of the proposed method are a fast response, non-invasive sampling, total harmonic distortion reduction, more efficient usage of PV modules and a simple training of the ANFIS algorithm. To demonstrate the effectiveness and accuracy of the ANFIS in relation to the MPPT algorithm, a practical sample case of 10 kW PV system and its measurements are used as a model for simulation. Modelling and simulations are performed using all available components provided by technical data. The results obtained from the simulations point to the more efficient usage of the ANFIS model proposed as an MPPT algorithm for PV modules in comparison to other existing methods.
Clean energy sources such as photovoltaic (PV) panels
are widely employed. However, their performance is affected by
the surroundings. A hybrid optimization technique that
comprised an ant lion optimizer (ALO) and artificial neural
network (ANN) is presented in this study, to forecast the PV cell
temperature and output power. The optimizer's major purpose
was to create and improve an ANN approach that was based on
training and forecasting. The ALO was used as MVO and GA to
obtain the optimal hidden layers neurons number, weights, and
biases, of the proposed ANNs. The accuracy of the multilayer
feed forward neural networks (MFFNN) was evaluated using the
data from the MFFNN-MVO, MFFNN-GA and MFFNN-ALO
models. The panel output power and temperature were regulated
by three variables: solar irradiation, ambient temperature, and
wind speed. The Saudi Arabia, Shaqra City PV station with 4-
kW output power is the source of the two years testing and
training. For the MFFNN-GA, MFFNN-MVO, and MFFNNALO models, the NRMSE for DC power predicting compared to
2019 observ
This research presents tracking the maximum power of a photovoltaic to control a five-level inverter, a cascade type connecting a single-phase grid-connected system with a fuzzy logic control model. Maximum power tracking control In this research, the principle of controlling the maximum current amplitude of the photovoltaic multiplied by the sine signal per unit that used as a reference current compared to the grid current. Signal comparison with the PID controller allows the creation of five levels of PWM of cascade control of five-level inverter connects single-phase grids. The results of the simulation test using the program MATLAB/Simulink to compare with the generated prototype found that the fuzzy logic principle was used to control the maximum power tracking conditions of the P&O method, when the amount of radiation light intensity decreases suddenly, making it possible to track the maximum power of the photovoltaic. Also, when the inverter connected to the grid by controlling the power angle to compare results between the simulation and the prototype — found that the current flowing into the grid increases according to the power angle control. Resulting in a nearby waveform, sine wave and an out of phase angle to the grid voltage because the system is in the inverter mode, and the harmonic spectrum of the grid currently has total harmonic distortion (THD) is reduced as an indication of the proposed system can be developed and applications.
A Reliable Tool Based on the Fuzzy Logic Control Method Applying to the DC/DC...phthanh04
Solar energy performs an important role in electric energy based on renewable energy generation systems when referring to
clear energy. Systems for harvesting renewable energy frequently use DC/DC converters, especially solar photovoltaic systems. The
DC/DC boost converter has been used for converting the output voltage from the solar PV system to the required voltage rating of the
utility grid under the disturbance in the photovoltaic temperature and irradiation level. Because of that, a new maximum power point
tracking based on the fuzzy logic controller (MPPT-FLC) algorithm applying the DC/DC boost converter is developed. The proposed
approach aims toward improving the PV system's performance and tracking effectiveness. This aim can be achieved by adjusting the
DC/DC boost converter's duty cycle to ensure that the PV system operates close to its MPP under varying environmental conditions. The
effectiveness of the proposed method is verified in the off-grid PV system under conditions of the change of irradiation and temperature,
and the comparison of between the proposed method, the incremental conductance (INC), perturb and observe (P&O), and modified P&O
methods is also made. The obtained simulation results show that the MPPT capability significantly improved and achieved the highest
MPPT efficiency of 99.999% and an average efficiency of 99.98% in total when applying the proposed method.
We introduce in this paper a new FPGA-based Maximum Power Tracker for photovoltaic systems. The developed approach targets to modify the perturb and observe in view of reaching rapid tracking and achieving excellent accuracy, while keeping the stability performance and the reduced complexity. To perform this improvement, an automatic and smart two steps switcher is integrated, in addition inputs FIR filters are incorporated. Therefore, a high sampling frequency is attained, and consequently the tracking speed is improved. MATLAB simulations and the Xilinx FPGA implementation results show that the improved approach reaches a performance very close to the recently published MPPT methods, with lesser complexity.
The power supplied by photovoltaic DC–DC converter is affected by two factors, sun irradiance and temperature. Therefore, to improve the performance of the PV system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique present some difficulties in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). In this paper, we present a comparative study of the PV standalone system which is controlled by three techniques. The first one is conventional based on the observation and perturbation technique, the other are intelligent based on fuzzy logic according Mamdani and Takagi-Sugeno models. The investigations show that the fuzzy logic controllers provide the best results and Takagi-Sugeno model presents the lower overshoot value.
An efficient scanning algorithm for photovoltaic systems under partial shadingIJECEIAES
This paper proposes a new technique of maximum power point tracking (MPPT) for a photovoltaic (PV) system connected to three phase grids under partial shading condition (PSC), based on a new combined perturb and observe (P&O) with scanning algorithm. This new algorithm main advantages are the high-speed tracking compared to existing algorithms, high accuracy and simplicity which makes it ideal for hardware implementation. Simulation was carried on MATLAB/Simulink. Results showed the effectiveness in speed and accuracy of our algorithm over the existing ones either during standard condition (STC) or PSC. Furthermore, conventional direct power control (DPC) was applied to synchronize successfully the injected power with the grid, which makes our algorithm global and works efficiently under severe conditions.
Time-domain harmonic extraction algorithms for three-level inverter-based sh...IJECEIAES
Power quality is a major consideration in all office equipment, manufacturies and residential home appliances. Harmonic distortion is one of the crucial power quality issues. In order to mitigate the harmonic distortion, the performance of shunt active power filter (SAPF) is judged in terms of the accuracy and response time of its designed controller. In this context, the controller consists of three parts: harmonic extraction, switching control, and DC-link capacitor. The harmonic extraction technique serves the major role of deriving the required reference current to ensure successful mitigation of current harmonics by SAPF. Among the existing techniques, harmonic extraction algorithms based on time-domain approaches are most widely applied as they offer simple implementation features with increased speed and reduced computational burden. This paper presents detailed investigation and analysis regarding the performance of two famous time-domain harmonic extraction techniques namely, synchronous reference frame (SRF) and instantaneous power (PQ) theory. Extensive simulation work is conducted in MATLAB-Simulink platform under two conditions, which are, steady-state conditions and dynamic-state conditions, considering various highly nonlinear loads. For evaluation purposes, each control algorithm is incorporated into the controller of a three-phase SAPF, developed using a three-level neutral-point-clamped (NPC) inverter. Comprehensive results are provided to confirm mitigation performance of the SAPF utilizing each harmonic extraction algorithm.
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
1. Abstract
Nowadays, it has been a developing consideration towards utilization of photovoltaic (PV) system. This paper proposes the
Adaptive Neuro-Fuzzy Inference System (ANFIS) and an integrated offline Genetic Algorithm (GA) to track the PV power
based on different circumstances due to the various climate changes. Training data in ANFIS are optimized by GA. The
proposed controller is accomplished and studied applying Matlab/Simulink software. The results show minimal error of
Maximum Power Point (MPP), Optimal Voltage (Vmpp
) and superior capability of the suggested method in MPP tracking.
*Author for correspondence
Indian Journal of Science and Technology, Vol 8(10), 982–991, May 2015
ISSN (Print) : 0974-6846
ISSN (Online) : 0974-5645
Implementing GA-ANFIS for Maximum Power Point
Tracking in PV System
Alireza Rezvani1
*, Maziar Izadbakhsh1
, Majid Gandomkar2
and Saeed Vafaei2
1
Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran;
alireza.rezvani.saveh@gmail.com
2
Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
Keywords: ANFIS, GA, MPPT, Photovoltaic System.
1. Introduction
For tracking the incessantly diverging the MPP of the
solar array, MPPT control approach acts a significant part
in the PV arrays1-3
.
The rifest methods are the perturbation and observa-
tion (P&O) algorithm3,4
, Incremental conductance
(IC)5,6
fuzzy logic controller (FLC)7,8
and artificial net-
works (ANN)9-11
. The P&O and IC methods are widely
applied in the MPPT due to its simplicity and easy exe-
cution, one of the drawbacks of this technique that it is
precision in steady-state condition is low since the per-
turbation operation would cause to oscillate the operating
point of the PV module around the MPP that wastes
the energy. When perturbation step size is minimized,
variation can be decreased, but a smaller perturba-
tion size decelerates the speed of MPPT. As well as, the
rapid changing of weather condition affects the output
power and this method fails to track easily the MPP12,13
.
Inrecentdecade, FCLisemployedfortracking the MPP
of PV module since it canbeconsidered robust,simplein
design because they do not need knowleminimal necessity
of the mathematical model dge of the accurate model and
Nevertheless, the FLC depends on deliberate election of
parameters, explanation of membership functions and
fuzzy rules. The effectiveness of FLC technique requires
specialist science and testing in choosing membership
functions and parameters. Some other weakness of FLC
is complex algorithms which lead in the high cost of
implementation14,15
. To overcome these weaknesses, new
methods such as ANN have been applied. The applica-
tion of ANN in different subjects has been increasing as it
gives an advantage of doing on non-linear tasks. ANN is
based on learning process and does not need to be repro-
grammed16,17
.
Neural network with approximation of the MPP in
photovoltaic module uses inputs such as environmental
conditions, PV irradiance, wind velocity, temperature and
time parameter which have been provided in18
. Besides,
this paper shows the number of neurons is kept as 5 in
the hidden layer and the output estimates the maximum
power. Maximum power prediction has been carried out
by comparisons between neural network and multiple
regressions. One of the problems in18
is that the results of
ANN are heavily dependent on preliminary selection of
training data.
2. Alireza Rezvani, Maziar Izadbakhsh, Majid Gandomkar and Saeed Vafaei
Indian Journal of Science and Technology 983Vol 8 (10) | May 2015 | www.indjst.org
The modeling and performance of the MPP in PV
module based on RBFN have been provided in16
. In com-
parison with traditional P&O method, RBFN is faster and
also has less fluctuation in performance point.
ANN can be considered as a best method for mapping
inputs-outputs of non-linear functions, but it lacks subjec-
tive sensations. In the other words, fuzzy logic approach
has the capability to transform linguistic and mental
data into numerical values. However, the determination
of membership functions and FLC rules depends on the
previous knowledge of the system. Neural networks can
be integrated with fuzzy logic and through the combina-
tion of these two smart tools, a robust AI technique called
ANFIS can be obtained19-21
.
In this study first, the 360 data of Temperature and
irradiance as the inputs data are applied to GA and Vmpp
corresponding to the MPP delivery from the photovoltaic
system, afterward the optimum values are implemented
for training the ANFIS.
The remnant of this article is formed as follows: structure
of photovoltaic module has been presented in part 2. The
GA method and ANFIS structure are explained in part
3. The simulation results have been presented in part 4.
Finally, conclusion described in part 5.
2. Structure of PV System
In Figure 1, the PV cell equivalent circuit is depicted.
Characteristic of one solar array is reported as following
equations (1):
3. Genetic Algorithm Technic and
ANFIS
Figure 1. Structure of PV solar cell.
S s
pv 0
t P
V R I V R I
I I - I exp -1 -
V n R
+ +
=
(1)
Where, I represents the photovoltaic current, V repre-
sents the photovoltaic voltage, Ipv is the light generated
current, the ideality factor can be represented by n, Rsh
and Rs are the parallel and series resistance. Vth is the
thermal voltage of diodes. The name-plate details are
reported in Table 1.
Table 1. Red sun 90w module
IMP
( Rated current) 4.94 A
VMP
( Rated voltage) 18.65V
PMAX
(Rated power) 90W
VOC
( Open circuit voltage) 22.32
ISC
( Short circuit current) 5.24
NP
(Parallel cells number) 1
NS
(Series cells number) 36
3.1 Applying of GA
ANFIS and GA methods are used to follow the opti-
mal point for MPP in environmental circumstances.
Besides, genetic algorithm is implemented for opti-
mal values. Then, these optimal values are applied for
training ANFIS22,23
. Implementing of GA is as follows24
:
1. the objective function and recogniz-
ing the design parameters are determined,
3. Implementing GA-ANFIS for Maximum Power Point Tracking in PV System
Indian Journal of Science and Technology984 Vol 8 (10) | May 2015 | www.indjst.org
2. the initial population is defined, 3. determining the size
of population and applying objective function, 4. con-
ducting convergence test.
Objective function of genetic algorithm is used for its
optimization as following: detecting the optimum X= (X1
,
X2
, X3
,..., Xn
) to put the F(X) in the maximum value, that
the design variable number is considered as 1. X is the
design variable equal to array current and also, F(X) is the
array output power which should be maximized22
. The
power should be arranged based on the array current (IX
)
to elect the objective function. The GA parameters have
been given in Table 2.
X(X) XF V *I=
..........................(2)
X SC0 I I< < .........................(3)
Table 2. The parameters of GA
Design Variable Number 1
Size of population 20
Crossover constant 80%
Mutation rate 10%
Maximum Generations 20
3.2 ANFIS Systems
An adaptive neural network has the advantages of learning
ability, optimization and balancing. However, a FLC based
on rules constructed by the knowledge of experts20,21.
The good performance and effectiveness of FLC have
been approved in nonlinear and complicated systems.
ANFIS combines the advantages of using adaptive neu-
ral network and FLC. ANFIS makes use of Sugeno. Fuzzy
inference system (FIS), a prevalent rule set is obtained
with 2 fuzzy if-then rules by the following Equations.
The fuzzy rules can typically reported as follows:
Rule 1: If x is A1 and y is B1; afterward
1 1 1 1= + +f p x q y r (4)
Rule 2: If x is A2 and y is B2; afterward
2 2 2 2= + +f p x q y r (5)
Which x and y can be considered as the inputs and f is the
output. [pi, qi, ri] are called the consequent parameters,
i = 1, 2.. The ANFIS structure of the above statements is
shown in Figure 2.
Layer 1: this layer consists of an adaptive node with a
node function. We have:
Output of this layer is its membership value. Membership
functions for A can be any proper parameterized mem-
bership function. Each parameter is regarded as a default
parameter.
Layer 2: this layer has been called with an “n” and
the output of each node is the product of multiplying all
incoming signals for that node. These nodes perform the
fuzzy AND operation, and we have:
Layer 3: Each node in this layer has been labeled with an
“N”. Nodes calculate the normalized output of each rule.
Then we have:
3,
1 2
1,2= = =
+
i
i i
W
Q w i
W W (9)
Where, Wi is the firing strength of that rule.
Layer 4: Each node in this layer is associated with a node
function. Then we have:
( )4, = = + +i i i i i i iQ w f w p x q y r
(10)
Where, Wi represents the normalized firing strength of
the third layer and {pi, qi, ri} are parameters sets of the
node i.
Layer 5: The single existing node in this layer is labeled as
Σ. It computes the sum of all its input signals and sends
them to the output section.
5, = =
∑
∑
∑
i ii
i i i
i ii
w f
Q w f
w
(11)
4. Alireza Rezvani, Maziar Izadbakhsh, Majid Gandomkar and Saeed Vafaei
Indian Journal of Science and Technology 985Vol 8 (10) | May 2015 | www.indjst.org
Where, Q5i
is the output of the node (i) in the fifth layer.
For this reason, first, all existing rules will be established
in the layer 1. For example, if we have two inputs, each
of which has three membership functions, then we must
form 9 rules.
That would be as follows in Figure 3.
Figure 2. ANFIS architecture.
Figure 3. Typical ANFIS structure.
4. Simulation Result
The mixture of least squares and gradient descent tech-
niques are constructed the hybrid learning algorithm.
The input of the ANFIS can be considered irradiation
and temperature and output is Vmpp
corresponding to the
MPP delivery from the PV system. Then, the output volt-
age of photovoltaic module with ANFIS output voltage
was deducted to obtain the error signal. Then, through a
PI controller, this error signal was given to a pulse width
modulation (PWM) block. In Figure 4, the diagram of
the presented MPPT is demonstrated. PV module was
designed in order to obtain optimum values by genetic
algorithm. A set of 360 data was put to temperature and
irradiance as inputs shown in Figure 5(a) and the output
5. Implementing GA-ANFIS for Maximum Power Point Tracking in PV System
Indian Journal of Science and Technology986 Vol 8 (10) | May 2015 | www.indjst.org
was Vmpp
corresponding to the MPP as depicted in Figure
5(b). Then these optimum values were utilized for train-
ing the ANFIS. By following Figure 5(a), all input were
360 data in which ANFIS model utilizes set of 330 data
was for training and also, a set of 30 data is utilized to test
the ANFIS. Input temperature ranged from 5 to 55°C in
the steps of 5° C and irradiance varied from 50 to 1000
(W/m2
) in the steps of 32 (W/m2
).
Figure 4. Proposed MPPT scheme.
(a)
Vmpp
corresponding to MPP
sponding to MPP
MPP
ANFIS input structure is shown in Figure 6. It includes
five layers. The two inputs represent irradiation and tem-
perature; that have 3 membership functions. In Figure 7,
the structure of the solar irradiance is shown and also, the
structure of the temperature is illustrated as Figure 8.
They have 9 fuzzy rules in total as shown in Figure 9; these
rules have a unique output for each input.
The network is trained for 10,000 epochs. After the
training process, output data should be very close to the
target outputs as shown in Figure 10.
uts data of irradiation and temperature, (b) Vmpp cor-
responding to MPP
irradiationandtemperature,(b)Vmpp
correspondingtoMPP
n and temperature, (b) Vmpp corresponding to MPP
rature, (b) Vmpp corresponding to MPP
6. Alireza Rezvani, Maziar Izadbakhsh, Majid Gandomkar and Saeed Vafaei
Indian Journal of Science and Technology 987Vol 8 (10) | May 2015 | www.indjst.org
(b)
Figure 5. Data: (a) Inputs data of irradiation and temperature, (b) Vmpp corresponding to MPP.
Figure 6. ANFIS controller structure.
Figure 7. Solar irradiance membership function.
7. Implementing GA-ANFIS for Maximum Power Point Tracking in PV System
Indian Journal of Science and Technology988 Vol 8 (10) | May 2015 | www.indjst.org
Figure 8. Temperature membership functions.
Figure 9. Fuzzy rules.
Figure 10. The output of the ANFIS with the value of target data
8. Alireza Rezvani, Maziar Izadbakhsh, Majid Gandomkar and Saeed Vafaei
Indian Journal of Science and Technology 989Vol 8 (10) | May 2015 | www.indjst.org
Figure 11. ANFIS output with the value of target data
Figure 12. Vmpp
error percentage.
Figure 13. ANFIS test output with the value of target data Data.
9. Implementing GA-ANFIS for Maximum Power Point Tracking in PV System
Indian Journal of Science and Technology990 Vol 8 (10) | May 2015 | www.indjst.org
According to Figures 11 and 12, Vmpp was compared
with the target values and in Figures 13-15 the output of
ANFIS test was compared with the target values, depict-
ing a negligible training error of about 1.4%. The ANFIS
based temperature and irradiation show best outcomes
with minimum error and the output power is optimized
with the assist of the GA method.
Figure 14. The output of the ANFIS test Vmpp with the value of target data.
Figure 15. Test data error percentage.
5. Conclusion
To track MPP of PV system GA-ANFIS method was
applied. With the assist of this technique, the PV module
was able to increase the production of the output power
at an optimal solution under various circumstances.
The GA was implemented to supply the optimal voltage
corresponding to the MPP for each environmental cir-
cumstances.
Then; optimized values were used for training the ANFIS.
For various conditions the proposed method was verified
and found that the error percentage of Vmpp between
0.05% to 1.46%. Incrementing the number of the training
data could be diminished Error of ANFIS.
6. References
1. Villalva MG, Gazoli JR, Filho E. Comprehensive approach
to modeling and simulation of photovoltaic arrays. IEEE
Transactions on Power Electronics. 2009; 24(5):1198–1208.
2. Shah R, Mithulananathan N, Bansal R, Lee K.Y, Lomi
A. Influence of large-scale pv on voltage stability of sub-
transmission system. International Journal on Electrical
Engineering and Informatics. 2012; 4(1):148–161.
10. Alireza Rezvani, Maziar Izadbakhsh, Majid Gandomkar and Saeed Vafaei
Indian Journal of Science and Technology 991Vol 8 (10) | May 2015 | www.indjst.org
3. Salas V, Olias E, Barrado A, Lazaro A. Review of the
maximum power point tracking algorithms for stand-
alone photovoltaic systems. Solar Energy Materials and
Solar Cells 2006; 90(11):1555–1578.
4. Femia N, Petrone G, Spagnuolo G, Vitelli M. Optimization
of perturb and observer maximum power point tracking
method. IEEE Transactions on Power Electronics. 2005;
20(4):963–973.
5. Chu CC, Chen CL. Robust maximum power point tracking
method for photovoltaic cells: a sliding mode control
approach. Solar Energy. 2009; 83(8):1370–1378.
6. Liu FF, Duan S, Liu B, Kang Y. A variable step size INC
MPPT method for PV systems. IEEE Trans. Industrial
Electronics. 2008; 55(7):2622-2628.
7. ALTIN N. Type-2 fuzzy logic controller based maximum
power point tracking in photovoltaic systems. Advances in
Electrical and Computer Engineering. 2013;13(3):65–70.
8. Bouchafaa F, Hamzaoui I, Hadjammar A. Fuzzy logic
control for the tracking of maximum power point of a PV
system. Energy Procedia 2011; 6(1):152–159.
9. Veerachary M, Senjyu T, Uezato K. Neural-network-
based maximum-power-point tracking of coupled
inductor interleaved-boost-converter-supplied PV system
using fuzzy controller. IEEE Transactions on Industrial
Electronics 2003; 50(4):749–758.
10. Rai AK, Kaushika ND, Singh B, Agarwal N. Simulation
model of ANN based maximum power point tracking
controller for solar PV system. Solar Energy Materials and
Solar Cells. 2011; 95(2):773–778.
11. Sundarabalan CK, Selvi K. Power quality enhancement
in power distribution system using artificial intelligence
based dynamic voltage restorer. International Journal on
Electrical Engineering and Informatics. 2013; 5(4):433–
446.
12. Fangrui L, Duan S, Liu F, Liu B. A variable step size INC
MPPT method for PV systems. IEEE Trans. on Industrial
Electronics. 2008; 55(7):2622–2628.
13. Esram T, Chapman PL. Comparison of photovoltaic
array maximum power point tracking techniques. IEEE
Transactions on Energy Conversion. 2007; 22(2):439–449.
14. Simoes MG, Franceschetti NN. Fuzzy Optimisation based
control of a solar array system. Electric Power Applications.
1999; 146(5):552–558.
15. Huh DP, Ropp ME. Comparative study of maximum
powerpoint tacking algorithm using an experimental,
programmable, maximum power point tracking test
bed. Proceedings of 28th IEEE Photovoltaic Specialists
Conference. 2000; pp.1699–1702.
16. Amoudi AL, Zhang L. Application of radial basis function
networks for solar-array modeling and maximum power-
point prediction. IEE Proceedings on Generation,
Transmission and Distribution. 2000; 147:310–316.
17. Zhang L, Bai YF. Genetic algorithm-trained radial basis
function neural networks for modeling photovoltaic
panels. Engineering Applications of Artifcial Intelligence.
2005; 18: 833–844.
18. Hiyama T, Kitabayashi K. Neural network based estimation
of maximum power generation from PV module using
environment information. IEEE Transactiom on Energy
Conversion.1997; 12( 3):241–247.
19. Aldobhani AMS, John R. Maximum power point tracking
of PV system using ANFIS prediction and fuzzylogic
tracking. Proceedings of the International Multiconference
Of Engineeris and Computer Scientists IMECS. 2008; 19–
21.
20. Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft
computing: a computational approach to learning and
machine intelligence.1997; 8(5):335–368.
21. Patcharaprakiti N, Premrudeepreechacharn S. Maximum
powerpoint tracking using adaptive fuzzylogic control for
grid connected photovoltaic system. PESW. 2002; 1(2):
372–377.
22. Rezvani A, Izadbakhsh M, Gandomkar M. Enhancement
of hybrid dynamic performance using ANFIS for fast
varying solar radiation and fuzzy logic controller in high
speeds wind. Journal of Electrical Systems. 2015; 11(1):11–
26.
23. Rezvani A, Izadbakhsh M, Gandomkar M, Saeed vafaei.
investigation of ANN-GA aniques for photovoltaic system
in the grid connected mode. Indian Journal of Science and
Technology. 2015; 8(1):87–95.
24. Yang J, Honavar V. Feature subset selection using a genetic
algorithm. IEEE Intelligent Systems. 1998; 13(2):44–49.