Power enhancement in shading prone photovoltaic system through termite queen algorithm

• The Photovoltaic power is cleaner and greener one which aids in mitigating the carbon emissions. The photovoltaic system’s power output is subject to variation based on the irradiation. • The variance in the power output is optimized by power trackers called MPPTs. However their performance is hindered when the irradiance is non-homogenous on the PV panels. • Therefore, an intelligent bio-inspired algorithm Termite queen algorithm (TQA) that mimics the division of labour in the termite colony, is used to optimize the power output and improve the performance of the MPPTs during partial shading conditions. • The algorithm adjusts to the rapidly varying irradiation pattern with high tracking efficiency. Termite Queen MPPT exhibits high adaptability and robustness. • The versatility of the proposed algorithm is tested by comparing it with well established algorithms and it is found that the proposed system has an edge over the prevailing ones. During partial shading conditions in photovoltaic (PV) systems, conventional maximum power point tracking (MPPT) algorithms often become trapped in local power peaks, resulting in reduced efficiency. In contrast, global search algorithms face challenges such as slow convergence, reduced accuracy, numerous tuning parameters, and poor stability. This research proposes a novel, nature-inspired Termite Queen Algorithm (TQA)-based MPPT that effectively identifies the global peak while avoiding local maxima. The division of labour within termite colonies—governed by the queen—enables efficient exploration of the search space, enhancing both accuracy and convergence speed. The TQA was applied to a 2000 W PV system frequently affected by shading. Results demonstrate that the TQA reliably locates the global power peak with faster convergence compared to other recently developed MPPT algorithms.