Global maximum power point tracking and cell parameter extraction in Photovoltaic systems using improved firefly algorithm

Optimization of power in Photovoltaic (PV) systems and extraction of cell parameters in PV cells using well-known metaheuristic techniques have been implemented by different researchers. However, under fast-changing irradiances and complex partial shading conditions, these algorithms underperform by getting trapped at the local maxima and with a poor convergence exploration, which in turn leads to a loss in energy supplied from a PV system. This paper proposes an innovative use of improved firefly algorithm (IFA) technique suitable for effective extraction of both the unknown cell parameters in PV cells and locating the global maximum power point (GMPP) that yields optimum power in PV array systems within the shortest possible time and with minimal loss in power when operated under different environmental conditions. To validate the effectiveness of the proposed IFA technique, six experimental analyses were conducted using four optimization techniques comprising of particle swarm optimization, cuckoo search algorithm, flower pollination algorithm, and the proposed IFA technique as case studies. Results achieved confirmed that IFA technique can be used to solve real-world problems in PV systems.