A New Modified Bacterial Foraging MPPT Technique with Dynamic Mutation Rates for Photovoltaic Systems under Partial Shading Conditions

This research article presents a novel approach to Maximum Power Point Tracking (MPPT) for photovoltaic systems, employing a modified bacterial foraging algorithm with dynamically adjustable mutation rates. This method is specifically tailored to address the challenges presented by partial shading conditions, ensuring efficient and rapid tracking of the MPP while preventing local optima entrapment. To evaluate the performance of this innovative technique, a comparative analysis is conducted against the original bacterial foraging algorithm and the grey wolf optimization algorithm, both commonly employed in MPPT applications. The modified algorithm incorporates a unique strategy that dynamically adapts mutation rates based on the algorithm's convergence behavior, enhancing the tracking accuracy from ۸۱.۳۱% to ۸۹.۳۹%. To validate the effectiveness of the proposed technique, extensive simulations are carried out using MATLAB Simulink, considering various partial shading scenarios commonly encountered in practical photovoltaic applications. It's worth noting that the shading scenario data were extracted from the NASA Worldwide Prediction of Energy website, specifically from the city of Ain El Ibel Djelfa irradiance records. The simulation results unequivocally demonstrate the superiority of the modified bacterial foraging MPPT technique over both algorithms in terms of tracking efficiency (۰.۴s to ۰.۹s) and robustness under partial shading conditions. The findings of this research offer valuable insights into the potential advantages of employing a modified bacterial foraging approach for MPPT applications. This innovative techniques with its ability significantly enhance its performance in real-world scenarios involving partial shading, positioning it as a promising choice for optimizing photovoltaic system efficiency and power output.

Keywords:

photovoltaic systems , Maximum power point tracking , partial shading , Bacterial Foraging Algorithm , Grey Wolf Optimization , Dynamic Mutation Rate