Self-adaptive enhanced learning differential evolution with surprisingly efficient decomposition approach for parameter identification of photovoltaic models.

• Surprisingly efficient decomposition approach is used to decompose unknown parameters for many different photovoltaic models, such as the SDM, DDM and PV module model. • Self-adaptive enhanced learning differential evolution—is designed to identify the nonlinear parameters obtained by decomposition, and then, by computing the derived matrix equations to obtain the linear parameters. • Considering the installation scenario of real photovoltaic generator sets, photovoltaic module models under different ambient temperatures and different irradiances are tested using the SaELDE. In order for the photovoltaic power generation system to convert energy with the highest efficiency after installation, the unknown parameters of the photovoltaic model need to be accurately set in advance. Therefore, in order to identify unknown parameters of photovoltaic models more reliably, effectively, and accurately, this paper first uses the decomposition approach that is extremely effective for different types of photovoltaic models, called matrix rotation decomposition. The unknown parameters of different photovoltaic models are decomposed into linear parameters and nonlinear parameters through matrix rotation decomposition approach. Then a simple and effective algorithm—Self-adaptive enhanced learning differential evolution (SaELDE)—is proposed to identify the decomposed nonlinear parameters. In SaELDE, there are three improvements: (1) the classification mutation learning mechanism is designed to make classified individuals evolve towards the promising direction; (2) the introduced crossover rate sorting approach effectively makes up for the neglected relationship between individuals and crossover rates; (3) combined with the population reduction strategy, it avoids the impact of the initial population setting on algorithm performance and balances the transition between the exploration and exploitation phases. Finally, the linear parameters are calculated by calculating the derived matrix equations according to the nonlinear parameters. Experimental results from a variety of common photovoltaic models, and even photovoltaic module models that need to face different temperatures and irradiance, show that the proposed SaELDE performs more accurately and has better robustness. In addition, the correlation coefficient of SaELDE is 1 under all conditions, which further proves the accuracy of the parameters extracted by SaELDE. [ABSTRACT FROM AUTHOR]