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. [4]. The non-linear and dynamic nature of the power-voltage characteristic in PV systems requires sophisticated maximum power point tracking (MPPT) algorithms. *Author for correspondenceThese algorithms vary based on factors like cost, efficiency, response time, required information, and the ability to track the global maximum power point (GMPP) during partial shading or rapidly changing environmental conditions, as well as the complexity of implementation. Traditional methods, such as Perturb and Observe (P&O), face limitations in convergence speed, oscillations around the MPP, and accuracy, especially when environmental conditions fluctuate [5,6].The current research paper is motivated by the inefficiencies and challenges identified in the existing literature. While various MPPT algorithms have been proposed, they differ significantly in terms of their efficiency, convergence time, complexity, and adaptability to changing environmental conditions. Most notably, they struggle with oscillations and prolonged time to converge to the GMPP, especially in scenarios like partial shading [7]. While the grey wolf optimization (GWO) algorithm demonstrates a promising exploratory nature and quick convergence capabilities, it is susceptible to persistent oscillations Re search Article