Walrus Optimization Algorithm for Multi-MPP Tracking Under Harsh and Variable Conditions: Simulation and Real-Time Validation

Renewable energy sources, especially solar energy, have seen a significant increase in installed capacity worldwide in recent years compared to other renewable energy sources. In solar energy systems, maximum power point (MPP) tracking (MPPT) algorithms are used to transfer the power obtained from the panel to the load/grid at the highest possible level. Classical MPPT algorithms track local maximum power points (LMPPs) under partial shading conditions (PSC). In many metaheuristic optimization algorithms, the behavior of searching for the optimum solution is determined by fixed or predefined rules. In this study, simulation and real-time testing of the walrus optimization algorithm (WaOA), which has been reported in the literature as a promising algorithm with a good exploration–exploitation balance and the potential for relatively low sensitivity to parameter settings, are carried out for photovoltaic (PV) systems. Two different scenarios were evaluated in simulation studies. In the first scenario, an off-grid PV system was evaluated under five different partial shading and rapidly changing environmental conditions. In the second scenario, the proposed WaOA algorithm was tested for grid-connected PV systems under two different scenarios. Furthermore, the performance of WaOA in PV systems has been experimentally verified through real-time tests using the Chroma 62020H-150S Solar Array Simulator. Both in the simulation studies and in real-time tests, it is observed that WaOA showed high success in PV systems.