Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

During partial shading conditions (PSCs), the efficiency of power transfer in a Photovoltaic (PV) system decreases significantly, which can result in the formation of hotspots in the PV array. Although the insertion of bypass diodes can alleviate this issue, it can result in multiple power peaks on the power-voltage (P–V) characteristics and complicate the process of maximum power tracking. To tackle this problem, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favourable results by avoiding convergence to local power peaks and reducing computation stress on the microcontroller. However, continuous research is required in this area due to variations in the performance of metaheuristic algorithms. Therefore, this work introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) for the successful capture of the Maximum Power Point (MPP) under various PS scenarios. The performance of AOA is evaluated against the other state-of-the-art algorithms such as particle swarm optimization (PSO), Jaya, and the newly proposed variant of Jaya called the adaptive Jaya (A-Jaya). The proposed MPPT method is analysed in MATLAB/Simulink software and validated using real-time results obtained from Typhoon Hardware-in-the-loop (HIL)-402 emulator. The proposed algorithm outperforms the previous algorithms, as evidenced by a comparison of the results in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations.