scholarly journals Artificial Neural Network Based MPPT Algorithm for Modern Household with Electric Vehicle

2022 ◽  
Vol 24 (1) ◽  
pp. C18-C26
Author(s):  
Ján Morgoš ◽  
Peter Klčo ◽  
Karol Hrudkay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electric Vehicle ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Artificial Neural ◽  
Power Point

This paper deals with implementation of artificial neural network in the maximum power point tracking (MPPT) controller algorithm for modern household where electric vehicle (EV) was purchased. The proposed MPPT algorithm was designed to achieve the best possible efficiency of the MPP (maximum power point) tracking and the best possible energy harvesting to charge the EV’s battery. The artificial neural networks have strong advantage in fast input to output response of signals and the finding of MPP is faster than in commonly used algorithms. In this article, the optimised simulation model based on artificial neural network will be introduced. The proposed artificial neural network algorithm was designed for non-shielded photovoltaic panels.

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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