A Novel Hybrid GGWO–Takagi Sugeno Kang Fuzzy Type 2 Based Maximum Power Point Tracking for Photovoltaic Systems Operating Under Partial Shading Conditions

In solar systems, determining the peak power point amidst variable weather conditions or disturbances poses a significant challenge for energy efficiency. In partial shade conditions, the occurrence of various peak levels in the power and voltage curves in the solar panels complicates the tracking of the maximum power point. Under these conditions, the maksimum power point often gets stuck at local maxima, hindering the ability to determine the global MPP. This paper introduces a hybrid Takagi-Sugeno-Kang type 2 fuzzy logic technique using genetic algorithms and grey wolf algorithms to regulate the photovoltaic maximum power point tracking in partial shade conditions. The suggested system has been simulated and evaluated with MATLAB/Simulink tools under circumstances of rapidly fluctuating irradiance and partial shade. Additionally, its performance is compared against the current GA and GWO algorithms in particular. It shows that the proposed GGWO-TSK Type 2 algorithm is superior to other methods with high efficiency up to 99.98%, fast conversion time and stability time under both uniform and variable irradiance conditions.