Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm

Evaluating the performance of photovoltaic panels inevitably involves having the exact model of solar cells. Different approaches to model solar cells have been proposed in literature which can generally be classified as either traditional or intelligent methods. To obtain the accurate model of such highly nonlinear systems, however, is still a challenging task, defying researchers. This study proposes an Improved Lozi Map based Chaotic Optimization Algorithm (ILCOA) algorithm to estimate the unknown parameters of the solar cells. Remarkable local and global searching abilities of the proposed algorithm give it a distinct edge over other optimization methods, enabling it to sift the whole search space for the global optimum. The efficacy of the proposed approach is finally highlighted by comparing its performance with those of three other algorithms including BMO, CWOA, and LCOA in terms of RMSE, Relative Error, Mean Absolute Error (MAE), Normalized MAE, and Mean Bias Error.