Swarm intelligence-based maximum power point tracking algorithms for partial shading in photovoltaic systems

Abstract

The rapid expansion of photovoltaic (PV) technology has increased the demand for efficient energy conversion, especially under partial shading conditions commonly found in real-world environments. Conventional maximum power point tracking (MPPT) methods, such as perturb and observe (P&O) and incremental conductance (INC), often perform poorly under fast-changing shading, resulting in power oscillations and energy losses. To address these limitations, intelligent optimization techniques, including machine learning and simulation-based methods, have been explored. Among them, swarm intelligence (SI) algorithms, inspired by collective behavior in nature, have demonstrated strong adaptability to dynamic operating conditions. Nevertheless, many existing MPPT approaches still face challenges in achieving fast convergence, global optimal tracking, and stable operation under severe partial shading. This study evaluates SI-based MPPT methods, focusing on particle swarm optimization (PSO), the firefly algorithm (FA), and a hybrid particle swarm–fireworks (PS-FW) approach. Comparative results show that SI-based techniques outperform conventional MPPT methods in terms of tracking accuracy, convergence speed, and stability. These improvements enhance the reliability and efficiency of PV systems, supporting sustainable energy generation and providing guidance for robust PV operation under diverse environmental conditions.