Genetic Algorithm-based MPPT for Partially Shaded Photovoltaic Systems

As we rely more and more on renewable energy, we need to come up with better ways to make photovoltaic (PV) systems work better, even when they face problems like partial shading. This article looks into how genetic algorithms (GAs) can be used to improve maximum power point tracking (MPPT), which is a key method for getting the most energy out of photovoltaic (PV) systems. Conventional MPPT methods often struggle to identify the global maximum power point under partial shading, leading to suboptimal performance. GAs, inspired by biological evolution, offer a promising solution by dynamically adapting to changing conditions. This study examines the application of GAs to MPPT, highlighting their ability to navigate complex voltage-power curves and achieve higher energy yields. We show that GA-enhanced MPPT is better at making PV systems more efficient overall through simulations and comparisons. These findings underscore the importance of sophisticated MPPT methods in advancing the sustainability and efficacy of solar energy technologies.