Performance Evaluation of PSO-Based Hybrid MPPT Techniques Using PUMA, SPIDER, and GWCA for Solar Photovoltaic Systems

Abstract

Efficient Maximum Power Point Tracking (MPPT) is crucial for enhancing the performance of photovoltaic (PV) systems, especially in dynamic and partially shaded environments. Conventional MPPT methods frequently experience slow convergence rates and are prone to getting stuck in local optima. This research investigates hybrid strategies that combine Particle Swarm Optimization (PSO) with three contemporary metaheuristic algorithms: Great Wall Construction Algorithm (GWCA), Spider Wasp Optimization (SWO), and Puma Optimizer (PO). The goal of these combinations is to improve tracking speed, precision, and overall system stability. A comparative analysis based on simulations is performed to evaluate their effectiveness in various environmental conditions. The findings indicate that the proposed hybrid techniques significantly enhance MPPT reliability and energy harvesting when compared to traditional methods. The Hybrid PSO_PO method delivers a balanced performance, achieving a tracking time of 0.0824 s and lowest steady-state oscillations of 72 W. Although it may not be the fastest or the most stable on its own, it effectively merges both elements. It reaches an efficiency of 99.88%, the shortest settling time of 1.7141 s, and a minimal peak power deviation of 144 W, guaranteeing precise MPP tracking under varying conditions.