Parameter estimation of photovoltaic models with memetic adaptive differential evolution

Abstract

Parameter estimation of photovoltaic (PV) models plays an important role in the simulation, evaluation, and control of PV systems. In the past decade, although many meta-heuristic methods have been devoted to parameter estimation of PV models and achieved satisfactory results, they may suffer from consuming large computational resources to get promising performance. In order to fast and accurately estimate the parameters of PV models, in this paper, a memetic adaptive differential evolution, namely MADE, is developed. The proposed MADE can be featured as: (i) the success-history based adaptive differential evolution is used for the global search; (ii) the Nelder-Mead simplex method is employed for the local search to refine the solution; and (iii) the ranking-based elimination strategy is proposed to maintain the promising solutions in the external archive. To verify the performance of our approach, it is applied to estimate the unknown parameters of different PV models, i.e., the single diode model, the double diode model, and the PV module. Experimental results obtained by MADE are compared with several state-of-the-art methods reported in the literature. Comparison analysis demonstrates that the proposed MADE exhibits remarkable performance on accuracy and reliability. It also consumes less computational resources than other compared methods.