• WASSILA ISSAADI Electrical Engineering Laboratory, Faculty of Technology, University of Bejaia, 06000 Bejaia, Algeria
  • SALIM ISSAADI 1022 Rue de Louvain Est, Montréal, Québec, H2M 2E8, Canada


Intelligent neural networks, Frequency sample rate, Smoothness of the power signal, Low ripple rate, Photovoltaic maximum power point tracking (MPPT), Photovoltaic (PV) system


The present work continues the previous article published in the International Journal of Energy (Elsevier, 2019). Our previous study aimed to develop a new and innovative method based on neural network algorithms to predict an instantaneous command. A new control strategy for photovoltaic systems was presented in [1]. This command is based on the neuronal network (NN) technique. To the best of our knowledge, this technique has never been used in this field for that objective. The authors of this work used it to synthesize control laws for electronic power converters.
It should be noted that the newly designed algorithm based on neural networks is expected to be more robust with a good performance concerning tracking speed and precision. Moreover, the present research work aims at providing a robust neural structure against noisy empirical data, thus allowing the prediction of a new command. Indeed, in the present work, we will examine the parameters affecting four MPPT controls in addition to the new neural network-based algorithm developed in [1].


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Électrotechnique et électroénergétique | Electrical and Power Engineering