A modified stochastic fractal search algorithm for parameter estimation of solar cells and PV modules

In this work, a modified stochastic fractal search algorithm is proposed for effectively estimate the unknown model parameters of the single diode model and the double diode model of solar cells and photovoltaic modules. This algorithm modifies the diffusion process and the update processes of the original stochastic fractal search algorithm and employs a population size reduction mechanism and thus simplifies the implementation of the original algorithm and achieves better performance for the parameter estimation of solar cells and PV modules. Firstly, the algorithm is tested on three widely used estimation benchmark cases and compared with other seven state-of-the-art algorithms. The proposed algorithm shows its advantages in the aspects of accuracy, convergence speed, and stability over the other studied algorithms. In 100 independent runs with the maximum number of fitness function evaluations equal to 50000, the proposed algorithm achieved the best known solutions with 100% probability and the standard deviation of the 100 RMSE values is of the order of 10(-17). Then, the proposed algorithm is used to estimate the unknown single diode model parameters and double diode model parameters of three different types of PV modules, i.e. Multi-crystalline S75, Mono-crystalline SM55, and Thin-film ST40 under different irradiance and temperature conditions, and the algorithm also achieves sufficiently accurate models. The root mean square of the error (RMSE) values between the obtained models and the actual current data are of the order of 10(-2) or 10(-3). In view of its effectiveness and practicability, the proposed algorithm can be used as a new tool for the parameter estimation of solar cells and PV modules.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).