Formulation and Evaluation of Firefly and Artificial Bee Colony Algorithms for Maximum Power Extraction of Photovoltaic Systems Under Partial Shade

The main aim of this research is to formulate and evaluate firefly and artificial bee colony metaheuristic algorithms to track the global maximum power point for photovoltaic (PV) system in partial shading condition. The review of related researchs group available approaches as classical and evolutionary algorithms-based techniques for the proposed application. However, the classical optimization technique-based approaches have common shortcomings of being unable to track the global maximum power point whenever partial shading and mismatching in PV panels happen, missing the correct tracking direction during fast radiation change and output oscillation during steady-state condition since they are perturbation based approaches even though they are simple and have less complexity. A number of research activities using artificial intelligence-based approaches inspired by human intelligence, swarm intelligence, and different animals’ natural intelligence have been proposed and implemented using a direct duty ratio control approach for the power electronic converter. In this research firefly and artificial bee colony-based algorithms are formulated for direct voltage control approach, implemented, and their performance is evaluated. The results revealed that the proposed approach can track the global peak accurately and reduce steady-state oscillations compared to the incremental conductance (InCod) classical approach implemented for comparison purpose.