Publication

Abstract : Variations in ambient temperature, solar irradiation, and environmental conditions significantly impact the output of photovoltaic (PV) systems. To maximize power generation, it is crucial to operate efficiently at the maximum power point (MPP) under changing environmental conditions. Maximum Power Point Tracking (MPPT) techniques are employed to identify the optimal operating point for harvesting the highest power. A novel Dynamic Drone Squadron Optimization (DDSO) technique that monitors the global MPP is presented in this paper. A novelty search mechanism where the drones are rewarded for discovering new operating points that yield higher power under Complex Partial Shading Condition (CPSC) difficulties is developed in this algorithm. This helps in avoiding local maxima and encourages the search for getting maximum power than the local maxima under partial shading conditions. An extensive comparison analysis has also been given between DSO (Drone Squadron Optimization), Grey Wolf Optimization (GWO) and Cuckoo Search Optimization (CSO), to show the effectiveness of the suggested method under various partial shading circumstances. Simulation results demonstrate the superior performance of the novel Dynamic DSO, with an average efficiency of 99.03 %, a settling time of 0.38 s, and oscillations limited to 3.14 %. Statistical analysis further reveals that these performance metrics differ significantly among the MPPT algorithms considered, underscoring the effectiveness of DDSO under varying conditions. The integrated PV-EV to grid system benefits from the enhanced MPPT capabilities of DDSO, ensuring a reliable and sustainable power supply even under fluctuating environmental conditions.