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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Hicham Ouldzira
Hassan First University of Settat
Control & Systems Engineering Electrical & Electronics Engineering

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Integration of artificial neural networks for multi-source energy management in a smart grid Eziitouni Jarmouni; Ahmed Mouhsen; Mohammed Lamhammedi; Hicham Ouldzira
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i3.pp1919-1927

Abstract

Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency Ezzitouni Jarmouni; Ahmed Mouhsen; Mohamed Lamhamedi; Hicham Ouldzira; Ilias En-naoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1276-1285

Abstract

Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the maximum power point, the latter based on a multi-layer neural network. The optimized multi layer perceptron (MLP) will ensure a fast convergence to the maximum power point with a low oscillation compared to the classical method.
Co-Authors Ahmed Mouhsen Eziitouni Jarmouni Ezzitouni Jarmouni Ilias En-naoui Mohamed Lamhamedi Mohammed Lamhammedi