Particle Swarm Optimization (PSO) Based Photovoltaic MPPT Algorithm under the Partial Shading Condition

Performance of solar photovoltaic (PV) system degrades considerably when the surface of the PV array is covered with shadows indicated by the reduction in the generated power. Partial shading condition causes the PV array to produce multiple peaks in PV’s characteristic curve causing the non-optimal power generation of the conventional maximum power point (MPPT) algorithm. Therefore, we proposed an optimization of PV system through the design of particle swarm optimization (PSO) algorithm in the MPPT in order to enhance the power extraction during shaded conditions. The evaluation of the performance of MPPT were carried out by comparing tracking time, tracking error and efficiency obtained by the PSO along with the two most used algorithms namely perturb and observe (P&O) and incremental conductance (IC). A partial shading generator function was designed to generate shading patterns randomly. The PV system consists of ten PV modules integrated with DC dummy load through DC-DC boost converter was simulated using MATLAB/SIMULINK. Simulation results show that the proposed PSO could obtain the global maximum power point (GMPP) while tracking performance of the P&O and IC MPPT both were stuck on the local maxima. Moreover, the optimization of PSO MPPT gave a promising result that the efficiency of the PV system was improved by around 4.66% on the dynamic shading conditions, while the MPPT’s tracking time is slowed down by 0.0025 s and 0.0105 s when its compared to the P&O and IC MPPT respectively.