关键词: Photovoltaic models , · Multi-verse optimizer , · Nelder–Mead simplex , · Iterative chaos map

Abstract: Extracting photovoltaic (PV) model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems. To accurately and reliably extract the unknown parameters of diferent PV models, this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method, INMVO. Quantitative experiments verifed that the proposed INMVO fueled by both mechanisms has more afuent populations and a more reasonable balance between exploration and exploitation. Further, to verify the feasibility and competitiveness of the proposal, this paper employed INMVO to extract the unknown parameters on single-diode, double-diode, three-diode, and PV module four well-known PV models, and the high-performance techniques are selected for comparison. In addition, the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically. Various evaluation metrics, such as root means square error, relative error, absolute error, and statistical test, demonstrate that the proposed INMVO works efectively and accurately to extract the unknown parameters on diferent PV models compared to other techniques. In addition, the capability of INMVO to stably and accurately extract unknown parameters was also verifed on three commercial PV modules under diferent irradiance and temperatures. In conclusion, the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of diferent PV models. Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.

Key words: Photovoltaic models , · Multi-verse optimizer , · Nelder–Mead simplex , · Iterative chaos map