An Enhanced Maximum Power Point Tracking Technique for Solar PV Systems under Partially Shaded Condition using Rao-3 Algorithm

In photovoltaic (PV) systems, extracting maximum power under varying environmental conditions remains a significant challenge, particularly when partial shading occurs. The classical Maximum Power Point Tracking (MPPT) methods often fail to distinguish the global peak from multiple local peaks introduced by shaded PV arrays. In this paper, a simple yet powerful MPPT technique based on the Rao-3 optimization algorithm is proposed. The proposed approach dynamically navigates the complex power-voltage characteristics of partially shaded PV arrays to accurately locate the global maximum power point (GMPP) without relying on extensive control parameters or derivative information. The performance of the proposed algorithm is systematically compared with Particle Swarm Optimization (PSO) and the classical Perturb and Observe (P&O) method, and Fuzzy Logic Controller (FLC). Simulation results demonstrate that while P&O exhibits fast tracking but often gets trapped in local maxima, FLC provides better adaptability but lacks precision in rapidly changing irradiance. PSO improves global tracking but suffers from high computational complexity, less tracking efficiency, and slower convergence. Simulation results demonstrate that the Rao-3 based MPPT significantly outperforms conventional methods in terms of tracking speed, efficiency, and stability under diverse shading patterns. The findings suggest that the proposed technique offers a reliable and computationally efficient solution for enhancing the energy harvest of PV systems in real-world conditions.