A Comprehensive Comparative Analysis of Mppt Techniques: Conventional, Soft Computing, and Metaheuristic Approaches

Abstract

Photovoltaic (PV) systems have gained widespread popularity as a clean and sustainable source of energy. However, their operational efficiency is often compromised under real-world conditions such as partial shading, which disrupts the generation of maximum power. This review paper investigates the application of meta-heuristic-based Robust Maximum Power Point Tracking (MPPT) algorithms to improve the operational efficiency of PV installations under partially shaded conditions. Various meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) are evaluated in terms of their speed, accuracy, and reliability for tracking the Maximum Power Point (MPP) under challenging environmental conditions. The study reveals that meta-heuristic-based MPPT algorithms offer significant advantages over traditional techniques such as Perturb and Observe (P&O) and Incremental Conductance (IncCond), notably in reducing oscillations around the MPP and improving the transient response. The paper further identifies the strengths and weaknesses of each algorithm, offering valuable insights for researchers and engineers striving to optimize partially shaded PV installations. The review concludes by suggesting future research directions that can further harness the potential of meta-heuristic algorithms for robust and efficient MPPT in solar PV systems.