Performance assessment of meta-heuristic MPPT strategies for solar panels under complex partial shading conditions and load variation

Weather variations present a major challenge for photovoltaic (PV) systems in obtaining the optimal output during maximum power point tracking (MPPT), particularly under partial shadowing conditions (PSCs). Bypass diodes are typically installed across the series-connected PV modules to avoid the occurrence of the hotspots. Consequently, the power curve exhibits several local peaks (LPs) and one global peak (GP). The conventional MPPTs typically become stuck in one of these LPs, presenting a significant decrease in both the power output and overall efficiency of the PV system. A major constraint of several optimization techniques is their inability to differentiate between the irradiance fluctuations and load alterations. In this study, we analyze seven different methods for MPPT. These include: the team game algorithm (TGA), social ki driver algorithm (SSD), differential evolution (DE), grey wolf optimization (GWO), particle swarm optimization (PSO), cuckoo search (CS), and the perturb and observe (P&O) method. These algorithms were applied in practice, and their effectiveness was experimentally demonstrated under different amounts of solar irradiation while maintaining a constant temperature. The results indicate that the CS and TGA approaches can accurately track the MPPT across various positions on the P-V curve. These methods achieve average efficiencies of 99.59% and 99.54%, respectively. Additionally, the TGA achieves superior performance with the shortest average tracking time of 0.92 seconds, outperforming the existing MPPT algorithms.