Identifying the parameters of photovoltaic cells using Gaussian bare-bone imperialist competitive algorithm with opposition-based learning mechanism

Extracting the precise parameters of photovoltaic (PV) cells has become very important for simulation, evaluation, control, and optimization of PV systems. However, it is still a challenging task to accurately and reliably extract the parameters of PV cells. To solve this difficult problem, in this paper, a new meta-heuristic algorithm called Gaussian bare-bone imperialist competitive algorithm with opposition-based learning (OBL-GBBICA) is proposed to extract the parameters of PV cells. To strengthen the exploring ability and speed up the convergence, opposition-based learning (OBL) is introduced into an imperialist competitive algorithm (ICA) for two considerations. First, OBL is adopted in the population initialization to produce a high-quality population. Second, the OBL is introduced into the assimilation step to guide ICA to explore more promising regions. The above improvements not only speed up the convergence of ICA but also enhance its searchability, which is beneficial to improving the accuracy and reliability of identification results. Experimental results show that OBL-GBBICA exhibits great superiority in extracting the PV cells parameters, compared with other methods in the literature.