A Comparative Study of Two Metaheuristic MPPT Techniques to Extract Maximum Power from PV Array under Different Partial Shading Patterns

Abstract

Partial Shading (PS) substantially affects the energy produced from the solar PV array where multiple maximum power points (MPPs) are clearly manifested on the P-V characteristic curves. These MPPs involve multiple local peaks (LPs) in addition to a unique global peak (GP). Due to the complex nonlinear PV characteristics curves that are produced under the PS phenomenon, conventional MPP tracking (MPPT) techniques are not capable for tracking the GP and may be trapped in LP. Therefore, it is essential to utilize sophisticated MPPT techniques based on AI techniques to successfully trace the GP. In this manuscript, two metaheuristic techniques for MPPT are investigated based on the Flower Pollination Algorithm (FPA) in addition to the Deterministic Particle Swarm optimization Algorithm (DPSOA). A comparative study has been performed to assess the dynamic performance of the PV array under various PS patterns. Based on the simulation results, both techniques can successfully trace the GP with rapid convergence speed and zero failure rate. Despite the FPA is capable for following the GP, the DPSOA is superior with lower convergence speed and less steadystate oscillation around the GP.