Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition

Abstract

This study presents a novel maximum power point tracking (MPPT) algorithm via dynamic leader based collective intelligence (DLCI) of PV systems affected by partial shading condition (PSC). Different from the conventional meta-heuristic algorithms, DLCI is consisted of multiple sub-optimizers, which can achieve a much wider exploration by fully collaborating the optimization ability of various searching mechanisms instead of a single searching mechanism. In order to achieve a deeper exploitation, the sub-optimizer with the current best solution is chosen as the dynamic leader for an efficient searching guidance to other sub-optimizers. Although the multiple sub-optimizers of DLCI will result in a higher computational complexity, it can offer an enhanced searching ability and a more stable convergence compared to that of conventional meta-heuristic algorithms. Since it does not reply on the system model, DLCI can be easily applied to other optimization tasks. Four case studies, including start-up test, step change in solar irradiation with constant temperature, gradual change in both solar irradiation and temperature, and daily field data of solar irradiation and temperature in Hong Kong, are undertaken. They attempt to evaluate the effectiveness and advantages of DLCI in comparison to that of conventional incremental conductance (INC) and eight typical meta-heuristic algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bees colony (ABC), Cuckoo search algorithm (CSA), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), and teaching–learning-based optimization (TLBO), respectively. Lastly, a dSpace based hardware-in-the-loop (HIL) test is carried out to validate the implementation feasibility of DLCI based MPPT technique. Both the case studies and HIL test demonstrate that the searching ability of DLCI can be significantly improved via an effective coordination between multiple sub-optimizers, which can make the PV system generate more energy (up to 36.64%) and smaller power fluctuation (up to 21.17%) than other methods with a single searching mechanism.