A Novel Maximum Power Point Tracking Technique for Photovoltaic Systems Operating Under Partial Shading Conditions

Optimizing the electrical operating point is crucial for photovoltaic (PV) systems due to the significant impact of environmental factors on their output power. Conventional maximum power point tracking (MPPT) algorithms often face challenges with local maximum (LM) traps when partial shading conditions (PSCs) are present. This paper introduces a novel MPPT algorithm, named Adaptive Cuckoo Search Algorithm combined with Perturbation Observation (ACSA&PO), specifically designed for PV systems operating under rapidly changing solar irradiance and PSCs. The CSA component of the algorithm is enhanced through multiple dynamic strategies to improve convergence towards the global maximum (GM). Subsequently, the PO method is employed with a small step size to rapidly converge to the GM. Several test cases are devised to evaluate the performance of the MPPT algorithm utilizing ACSA&PO. Comparative analysis with efficient metaheuristic algorithms, including PO, CSA, Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), confirms that the proposed algorithm exhibits notable characteristics such as shortened convergence time and high tracking efficiency across diverse weather conditions. In various test scenarios, the ACSA&PO-based algorithm achieves an additional energy extraction of 1.67%, with an average tracking efficiency of 99.818%, leading to a 43.9% improvement in tracking time.