REVISION OF FLOWER POLLINATION ALGORITHM FOR OPTIMIZING PHOTOVOLTAIC SYSTEM

Abstract The renewable energy revolution is essential considering the depleting threats and greenhouse gas production of conventional energy resources. Solar has the priority among renewables due to the availability of the source almost everywhere. Further, solar photovoltaic (SPV) technology is prominent due to the direct conversion of sunlight into electricity. However, the non-linear characteristics do not allow the SPV cell to produce determined power. Therefore, the trackers driven by algorithms are hired to take the operating point to a maximum power point (MPP). Only soft-computing MPP tracking (MPPT) algorithms can survive under partial shading conditions (PSC). One of the most renowned soft-computing algorithms is the flower pollination algorithm (FPA) because it effectively distributes locally and globally. Optimal utilization of the strengths of FPA to improve its performance in MPPT for SPV cells has been performed in this research. Results have shown around 86% improvement in tracking speed which is a huge achievement. Simulations have been performed in MATLAB.