EVOLUTIONARY OPTIMIZATION APPROACH FOR FINDING GPPP OF A PV ARRAY SYSTEM UNDER HETEROGENEOUS OPERATING CONDITION

Abstract

This research work investigates an evolutionary optimization approach for finding Global Peak Power Point (GPPP) of a Photovoltaic (PV) array system under Heterogeneous operating conditions. The presence of a by-pass diode introduces multiple peaks in the Power-Voltage (P-V) and multiple steps in Current-Voltage (I-V) characteristics of a PV array under Heterogeneous operating conditions. The Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO) techniques have been incorporated to determine the effective GPPP under shadow conditions. The effects of cognitive coefficient of individual particles (C1) and the social coefficient of all particles (C2) play a major role in finding the optimum solution in the search space of PSO are also studied. The BFO algorithm has a large number of control parameters when compared to PSO and it shows significant improvements in terms of convergence speed and final accuracy towards reaching the GPPP. The mathematical model of proposed PV system have been developed and simulated in Matlab-simulink environment to track the GPPP. The simulated results of Incremental Conductance (INC) method, PSO and BFO are evaluated and compared for different shading patterns of the PV array system. From the obtained results, it is found that the BFO algorithm gives considerable improvements than INC and PSO algorithms.