Optimizing the performance of a stand-alone PV system under non-uniform irradiance using Gray-Wolf and hybrid neural network AI-MPPT algorithms

Abstract

This paper introduces an improved gray-wolf optimization technique (EGWO) for a photovoltaic (PV) stand-alone system. The fundamental objective is to study non-uniform solar irradiance power mismatches in PV modules through modelling maximum power point tracker (MPPT) for increasing PV power output. An EGWO-MPPT detection algorithm for promoting the global peak between the multiple peaks is implemented, seeking for the optimum maximum energy from the PV system. Furthermore, a neural network-based MPPT optimizer has been modeled as a benchmark for our proposed system, showing the trade-off between time response and accuracy under a non-uniform irradiance profile.