Opposition-based JAYA with population reduction for parameter estimation of photovoltaic solar cells and modules

Abstract To efficiently increase the conversion of solar energy into electricity, it is vitally important to find the appropriate equivalent circuit parameters to execute the modelling, evaluation, and maximum power point tracking on photovoltaic (PV) systems in high quality and efficiency. In this study, an enhanced JAYA (EJAYA) algorithm is proposed for accurately and efficiently estimating the PV system parameters. In EJAYA, it consists of three main improvements: (i) A modified evolution operator, based on the tendency factor adaption, is introduced to increase the probability of approaching the victory. (ii) The simple deterministic population resizing method is incorporated to control the convergence rate during the search. (iii) EJAYA employs generalized opposition-based learning mechanism to avoid being trapped in local optima. Experimental results tested over several different PV models demonstrate the excellence of EJAYA on accuracy, stability, and convergence speed. Additionally, to further highlight the effectiveness of EJAYA, other different modules from the data sheet are tested at different temperature and irradiance. Consequently, EJAYA is superior to become an alternative for the parameter detection of PV cells and modules at various practical conditions.