Boosting slime mould algorithm for parameter identification of photovoltaic models

Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into electricity. For this purpose, this work proposes an advanced slime mould algorithm (SMA) integrated Nelder-Mead simplex strategy and chaotic map, called CNMSMA. Chaotic maps replace the random number rand that affects the choice of location updating strategy to improve the exploratory patterns. Also, Nelder-Mead simplex is introduced to reinforce the intensification capacity of the algorithm. The effectiveness of CCNMSMA has been verified in a single diode model, double diode model, and three diode models for RTC France solar cell and PVM 752 GaAs cell. Three commercial PV module models, which are the ST40, SM55, and KC200GT, are also utilized to verify the stability of CNMSMA under various temperatures and irradiances. The simulation results demonstrate that a developed SMA-based method can accurately extract the unknown photovoltaic solar cells' unknown parameters and achieve excellent convergence rapidity and stability performance. Also, no matter under insufficient irradiance or high-temperature conditions, CNMSMA is still without losing its accuracy and shows satisfactory stability. Accordingly, the proposed algorithm could act as a reliable and developed tool for extracting significant unknown parameters of photovoltaic models. This research will be supported by https://aliasgharheidari.com. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study presents an advanced SMA-based algorithm, CNMSMA, incorporating Nelder-Mead simplex strategy and chaotic map, for efficiently and accurately estimating the unknown parameters of photovoltaic solar cells, demonstrating excellent convergence rapidity and stability.