Artificial neural network photovoltaic generator maximum power point tracking method using synergetic control algorithm

Akoro, E., Tevi, G. J., Faye, M. E., Doumbia, M. L. et Maiga, A. S. (2020). Artificial neural network photovoltaic generator maximum power point tracking method using synergetic control algorithm. International Journal on Emerging Technologies, 11 (2). pp. 590-594. ISSN 0975-8364 2249-3255

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Résumé

In this paper, a new approach to find the maximum power point (MPP) of a PV generator based on a hybrid Artificial Neural Networks (ANN)-Synergetic Control Algorithm (SCA) has been proposed. In the first part, the optimal voltage and current of the PV generator are found by using ANN algorithm. The second part deals with the design of a Synergetic Controller Algorithm (SCA) which generates automatically the optimal duty cycle to control the boost converter. The complete PV system is implemented in MATLAB/Simulink software and the results are compared with those obtained using the conventional Perturbation and Observation (P&O) method. Simulation results reveal that the proposed ANN-SCA algorithm is more efficient than the P&O algorithm. © 2020, Research Trend. All rights reserved.

Type de document:	Article
Mots-clés libres:	Artificial Neural Network MPPT P&O method PV generator Synergic control algorithm
Date de dépôt:	22 déc. 2022 19:09
Dernière modification:	22 déc. 2022 19:18
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/10322

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