Hybrid Bio-Intelligence Salp Swarm Algorithm for Maximum Power Point Tracking (MPPT) of Photovoltaic Systems Under Gradual Change in Irradiance Conditions

This paper presents the applications and hybridisation of metaheuristic optimisation of Salp Swarm Algorithm (SSA) with conventional algorithm Hill Climbing (HC) known as hybrid (SSA-HC). This new algorithm is proposed for improving the tracking efficiency of maximum power point tracking (MPPT) strategy during the gradual change of irradiance in Photovoltaic (PV) systems. The metaheuristic SSA successfully tracks the global maximum power point tracking (GMPP) during uniform and partial shading conditions (PSC) with fast-tracking but fails to track the GMPP during the gradual change of irradiance. Furthermore, while the conventional HC fails to track GMPP during PSC and slow tracking under uniform conditions, it always succeeds in tracking GMPP during gradual irradiance changes. The objective of combining metaheuristic SSA with conventional HC to propose a new hybrid SSA-HC algorithm that can deal with and adapt to extreme changing environments (PSC and gradual change irradiance) in PV systems. To prove the efficacy and performance of the algorithm, the proposed hybrid SSA-HC algorithm is compared with the SSA algorithm. The results show that the proposed hybrid SSA-HC algorithm outperforms the SSA algorithm in terms of MPPT efficiency (ηMPPT) by improving power output. By combining the advantages of the SSA and HC, the proposed algorithm can successfully detect the large and small changes in the power of the PV systems.