scholarly journals PV Maximum Power-Point Tracking by Using Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Farzad Sedaghati ◽  
Ali Nahavandi ◽  
Mohammad Ali Badamchizadeh ◽  
Sehraneh Ghaemi ◽  
Mehdi Abedinpour Fallah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Maximum Power ◽  
Atmospheric Conditions ◽  
Maximum Power Point ◽  
Error Back Propagation ◽  
Point Tracking ◽  
Artificial Neural ◽  
Power Point

In this paper, using artificial neural network (ANN) for tracking of maximum power point is discussed. Error back propagation method is used in order to train neural network. Neural network has advantages of fast and precisely tracking of maximum power point. In this method neural network is used to specify the reference voltage of maximum power point under different atmospheric conditions. By properly controling of dc-dc boost converter, tracking of maximum power point is feasible. To verify theory analysis, simulation result is obtained by using MATLAB/SIMULINK.

scholarly journals Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid

2021 ◽  
pp. 57-66
Author(s):  
H. Sahraoui ◽  
H. Mellah ◽  
S. Drid ◽  
L. Chrifi-Alaoui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Artificial Neural ◽  
Power Point

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

scholarly journals Maximum Power Point Tracking Algorithms for Photovoltaic System

2021 ◽  
pp. 313-318
Author(s):  
M. Gobikha ◽  
S. Akila ◽  
M. Latha ◽  
R. Dhanlakshmi ◽  
A. Bhavani Sankar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Perturb And Observe ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Power Point

This paper helps us analyse three different MPPT techniques like Perturb and Observe, Incremental Conductance and Artificial Neural Network. As the output characteristic depends on temperature and irradiance, therefore the maximum power point tracking (MPPT) is not always constant. Hence it is necessary to ensure that the PV panel is operating at its maximum power point. There are many different MPPT techniques but, the confusion lies in selecting which MPPT technique is best as every algorithm has its own merit and demerit. In order to extract maximum power from PV arrangement, Artificial Neural Network algorithm is proposed. Algorithms are implemented using the Boost converter. Results of simulations are presented in order to demonstrate the effectiveness of Artificial Neural Network algorithm, when compared to Perturb and Observe (P&O) and Incremental Conductance (INC). To simulate the proposed system MATLAB/ SIMULINK power system tool box is used.

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