Global maximum power point tracking technique based on adaptive salp swarm algorithm and P&O techniques for a PV string under partially shaded conditions

Abstract

ABSTRACT A Bio-inspired salp swarm algorithm (SSA) based soft computing technique is easy to implement with a single control parameter, which is dependent on maximum iteration count. But the maximum iteration count required for the on-line/hardware search process like maximum power point tracking (MPPT) of photovoltaic (PV) system is not known initially. Wrongly estimated maximum iteration count results in the unnecessary exploration of search space even after reaching the global peak (GP) region, which is the main cause for the slow-tracking of global maximum power point (GMPP) by conventional SSA. Hence, In this paper, a modified SSA based MPPT is proposed with an adaptive control parameter, and it is named Adaptive SSA (ASSA) based global maximum power point tracking (GMPPT) technique. Further, to achieve faster tracking of GMPP, ASSA is used only to identify the GP region, and in that region, variable step size P&O continues tracking. During irradiance changes, search space is limited for the reinitialization of ASSA. The proposed hybrid algorithm is simulated and experimentally validated using a boost DC-DC converter as an MPPT controller with direct duty ratio control. The proposed GMPPT technique results in faster-tracking of GMPP with the combined effect of adaptive parameter tuning, GP region identification with ASSA, and variable step size P&O. From the simulation and experimental results, it is evident that the proposed GMPPT technique is more than 1.3 times faster as compared to the conventional SSA based MPPT technique.