Analysis and Performance Evolution of Novel Global Modified Flower Pollination Algorithm for Photovoltaic System under Partial Shading Condition

Maximum Power Point (MXPP) tracking controller is used in solar photovoltaic (PV) systems to maximize output under varying atmospheric conditions. The appearance of several peaks owing to partial shade and complicate the tracking procedure. Despite the widespread usage of conventional and soft computing approaches to handle MXPP tracking problems, due of the set step size of conventional methods, their performance is restricted, whereas Soft computing approaches, on the other hand, are limited by a lack of randomness once they reach a certain point of maximum power. As a result, a Modified Flower Pollination Algorithm (MFPA) is suggested in this study, which has the capacity to attain a global peak. The MFP A method's optimization process combines global and local searching in a single step, and it's a crucial component of the MXPP tracking application's success. The algorithm's robustness is tested using a uniform and partial shading patterns. Furthermore, the MFP A technique is subjected to a full performance evaluation via simulation, which is quantified using standard PSO methodologies. Furthermore, the MFP A technique is subjected to a full performance evaluation via simulation, which is quantified using standard PSO methodologies.