MPPT command enhancement based on an ameliorated grey wolf optimization algorithm for a standalone PV system

Abstract

Photovoltaic (PV) systems represent a promising renewable energy technology with the potential to decrease greenhouse gas emissions and mitigate climate change impacts. These systems produce electrical energy by converting solar irradiance. However, despite significant technological advancements, the conversion efficiency of these electrical generators remains relatively low, even under optimal environmental conditions [1-3]. Extensive research has been conducted to minimize losses across all components of PV systems. Despite these efforts, limitations in harnessing available power persist, primarily due to environmental conditions, inverter efficiency, and the algorithms that control the direct current to direct current (DC-DC) converters tasked with tracking the maximum power point (MPP) of the system. The non-linear and dynamic nature of the power-voltage characteristic in PV systems requires sophisticated maximum power point tracking (MPPT) algorithms.