Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm

Abstract

The global shift toward solar energy has resulted in the advancement of research into the manufacture of high-performance solar cells. It is critical to accurately model and identify the parameters of solar cells. Numerous models of solar cells have been presented thus far, including the single-diode, the double-diode, and the three-diode models. Every model contains a number of unidentified parameters, and numerous approaches for determining their optimal values have been published in the literature. The purpose of this article is to propose an efficient optimization technique, dubbed the Chimp Optimization Algorithm (ChOA), for estimating the model parameters of solar networks. The proposed ChOA outperforms state-of-the-art algorithms in terms of convergence rate, global search capacity, and durability. To demonstrate the proposed ChOA algorithm's efficiency, it is used to determine the parameters of several photovoltaic modules and solar cells. The result of ChOA is evaluated and compared with ten well-known optimization algorithms in the literature. Additionally, the performance of the ChOA algorithm has been evaluated in a practical application for parameter evaluation of three widely-utilized commercial modules, i.e., multi-crystalline (KC200GT), polycrystalline (SW255), and monocrystalline (SM55), under a variety of temperature and irradiance circumstances that result in alterations in the photovoltaic model's parameters. The results confirm the proposed algorithm's robustness and high performance.