Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency

Ezzitouni Jarmouni (Laboratory of Radiation-Matter and Instrumentation (RMI), Hassan First University of Settat)
Ahmed Mouhsen (Hassan First University of Settat)
Mohamed Lamhamedi (Hassan First University of Settat)
Hicham Ouldzira (Hassan First University of Settat)
Ilias En-naoui (Hassan First University of Settat)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 28, No 3: December 2022

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.