Hierarchal Optimization of Flower Pollination Algorithm for Solar Photovoltaic Systems

There has been a significant shift towards renewable energy sources due to increasing energy loads, waning fossil fuel reservoirs, increasing greenhouse gas emissions, and escalating energy costs. Among renewable sources, solar energy stands out as a globally available resource. Photovoltaics (PV) represents the most efficient technology for directly converting solar energy into usable energy. However, PV cells exhibit non-linear characteristics and may not perform optimally under certain weather conditions. To address this limitation, it is crucial to consistently operate PV cells at their global maximum power point (GMPP) under partial shading conditions (PSC), as multiple peaks in the characteristic curve present a challenge. An electronic circuit governed by a GMPP chasing algorithm is employed to tackle this issue. Despite their weaknesses, nature-inspired algorithms have proven to be the most effective in this regard. One such algorithm, the flower pollination algorithm (FPA), is widely used for GMPP chasing. A comprehensive analysis of the FPA has integrated a data arrangement (DA) technique into the existing framework to improve GMPP chasing efficiency and reduce time. This enhanced structure is referred to as the DA-FPA. Comparative evaluations of both algorithms have been conducted across various standard benchmarks. Results indicate that DA-FPA surpasses the FPA in terms of GMPP chasing efficiency and time under diverse weather conditions. MATLAB/Simulink is utilized for simulation purposes.