scholarly journals Neural Network Based MPPT Controller for Solar PV Connected Induction Motor

2018 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
T. Shanthi
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Maximum Power Point Tracking ◽  
Solar Panel ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Neural Network Controller ◽  
Power Point

<p>In this paper, Maximum Power Point Tracking Controller is designed based on Neural Network Controller (NNC). This controller will sense the speed of a single phase induction motor which is fed from solar panel. Maximum power point tracking (MPPT) algorithm are required in all photovoltaic (PV) system and in order to increase the efficiency of the system, Incremental Conductance algorithm which is an effective algorithm is used to extract maximum power from the solar panel which supplies an Induction motor of 1HP. To step up the voltage available from the solar panel, the SEPIC dc – dc converter is used. The main advantage of the converter is having non-inverted output. The converter acts as an interface between PV array and motor load. The entire system is modeled and simulated using MATLAB/Simulink  software.</p>

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.

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