Parameter extraction of solar photovoltaic module by using a novel hybrid marine predators – success history based adaptive differential evolution algorithm

Abstract

ABSTRACT The manufacturers of photovoltaic (PV) panel give the data of three major points on I–V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e., single-diode and double-diode models. Hence, several population-based metaheuristic techniques are proposed in the literature. However, there is a need of well-balanced algorithm which is used for extracting the parameters of the diode model of PV panels from the datasheet. In this paper, a novel hybrid algorithm is proposed by combing the best features of a recently developed Marine Predators Algorithm (MPA) and Success History based Adaptive Differential Evolution (SHADE) algorithm. The principal algorithm for obtaining the optimum solution is MPA. However, during the search process to enhance the best solution region, self-adaptive DE based on the successive history of parameters is used. The derived objective function ensures the zero error at three important points of the I–V characteristics. Hence, the parameters extracted by using proposed method results in the I–V curves which are passing through the all three important points. Only three parameters out of five in single-diode model, and five parameters out of seven are optimized with the proposed algorithm and remaining are calculated analytically to reduce the burden on metaheuristic algorithm. MATLAB programming is used to test the proposed parameter extraction by using hybrid Marine Predators – Success History based Adaptive Differential Evolution (MP-SHADE) algorithm, and the results are compared with the other state-of-the-art metaheuristic techniques. The single-diode and double-diode models of three types of panels (monocrystalline, polycrystalline, and thin-film) are derived by using MP-SHADE algorithm.