Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm

• Published in: Mathematics (Basel) Vol. 10; no. 2; p. 183
• Main Authors: Yu, Xiaobing, Wu, Xuejing, Luo, Wenguan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Adaptive algorithms; Adaptive search techniques; Alternative energy sources; Design; differential evolution; Evolutionary algorithms; Evolutionary computation; Exploitation; Food science; Genetic algorithms; Heuristic; Heuristic methods; hybrid algorithm; JAYA; Mutation; optimization; Optimization algorithms; Parameter identification; Perturbation methods; Photovoltaic cells; Solar energy
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10020183

As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10−4, 9.8294 × 10−4, 2.4251 × 10−3, 1.7298 × 10−3, and 1.6601 × 10−2. Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems.