A new method for parameter extraction of solar photovoltaic models using gaining–sharing​ knowledge based algorithm

Abstract

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.