High-efficiency swarm intelligent maximum power point tracking control techniques for varying temperature and irradiance

摘要

PV systems may suffer significant power loss caused by irregular irradiance patterns and changing temperatures. Moreover, partial shading (PS) is a major defect for existing maximum power point tracking (MPPT) controllers. This paper proposes efficient and robust MPPT controllers using novel Slime mould optimization (SMO) and improved salp swarm optimization algorithm (ISSA) to track GMPP for different PV array configurations. ISSA takes opposition based learning (OBL) and local search algorithm (LSA) to perform maximum exploration of the search space. Due to LSA, the oscillation of ISSA can be reduced to zero. Moreover, a novel SMO MPPT technique is introduced for MPPT tracking of PV systems under various weather conditions. The results of the proposed controllers are compared with those of well-known perturb and observe (P&O), particle swarm optimization (PSO), SSA, and cuckoo search optimization algorithm (CSA) MPPT techniques. Six distinct cases of varying temperature, field atmospheric data study, uniform irradiance, PS, and complex-PS are studied. Results and their analysis confirm that the proposed controllers have the highest transient and steady-state efficiency of up to 99.9% with the least tracking time of up to 130 ms while keeping the oscillation in the steady-state well below 1-2W.& nbsp; (c) 2021 Elsevier Ltd. All rights reserved.