Multiple learning JAYA algorithm for parameters identifying of photovoltaic models

Abstract

Extracting the optimum parameters of the solar photovoltaic (PV) model based on measured current–voltage data accurately, reliably, and quickly is a vital step to simulating, evaluating, and optimizing the model PV. Although several meta-heuristics have been proposed to extract photovoltaic parameters using experimental data, it suffers from slow convergence and poor efficiency which leads to poor solution quality. In this paper, a multiple learning JAYA (MLJAYA) approach has been proposed to extract the PV parameters under different operating conditions. The main advantage of multiple learning strategies introduced in the MLJAYA proposed is its ability to balance exploration and exploitation capabilities. The chaos perturbation mechanism is employed to improve the quality of the population to escape the local optimum and improve the accuracy of the basic JAYA algorithm. The structure of MLJAYA is very simple and requires only the size of the population and stop criterion. The efficacy of the proposed MLJAYA has been verified by including different PV modules models such as single diode, double diode, and PV module, and the results obtained was compared with different variants of JAYA and other state-of-the-art optimization algorithms. The best results of root mean square error (RMSE), and absolute error generated by MLJAYA are (RMSE = 9.8602E−04, SIAE = 1.781248E−02), and (RMSE = 9.8248E−04, SIAE = 1.76E−02) for RTC solar cell single diode and double diode respectively, and (RMSE = 2.4250748E−03, SIAE = 4.686375E−02) for Photowatt-PWP201. The results show the superior performance in terms of accuracy and reliability. Since, the MLJAYA show is a potential approach for extracting parameters from photovoltaic models.