Comparative Analysis of Maximum Power Point Tracking Algorithms for Standalone PV System Under Variable Weather Conditions

Renewable energy systems are becoming increasingly predominant in the current scenario, and Photovoltaic (PV) arrays are one of the most widely used renewable energy generation sources. The current-voltage characteristics of PV arrays are non-linear, necessitating the need for supervisory techniques in order to ensure that the array functions at maximum efficiency, which is performed by Maximum Power Point Tracking (MPPT) techniques. These techniques are categorized into classical, intelligent and optimization algorithms. This paper performs a comparative analysis between five different MPPT techniques belonging to these categories – Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). A standalone PV system interfaced with a Boost converter is simulated on MATLAB Simulink for the performance evaluation of the MPPT techniques. Solar energy is extremely susceptible to changes in local weather conditions, mainly variations in solar insolation levels. The designed system is tested against a varying insolation profile in order to examine the robustness of the MPPT techniques, with their operation efficiencies showcased.