An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell and Module Models

Abstract

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.