Abstract
Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are two commonly employed techniques in designing maximum power point tracking systems in photovoltaic (PV) farms. A mathematical formulation of the objective function is derived by implementing the maximum power theorem for load matching using the relationship between input and output impedances. This paper also proposes a novel Center-based Latin Hypercube (CLHS) initialization scheme for population-based algorithms; it is shown that for population initialization, the newly proposed technique of CLHS gives better results with a small population size. A comprehensive comparative study is conducted on DE and PSO algorithms in terms of control parameters, search components, and population initialization methods to determine the best algorithm with its corresponding optimal parameters settings and population initialization to solve a family of maximum power point tracking problems. The work shows that both algorithms are capable of tracking the maximum power point although the PSO is more effective over a small population size. In this study, in overall, 15,876 and 96,228 settings possibilities for DE and PSO respectively are investigated.