Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. (15th September 2018)

• Record Type: Journal Article
• Authors: Yu, Kunjie; Liang, J.J.; Qu, B.Y.; Cheng, Zhiping; Wang, Heshan
• Is Part Of: Applied energy. Volume 226(2018)
• Page Start: 408
• Page End: 422
• Publication Date: 2018-09-15
• Subjects: Parameter identification -- Photovoltaic model -- Backtracking search algorithm -- Multiple learning; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.06.010
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: MLBSA is proposed to identify the parameters of PV models. Multiple learning strategy aims to balance exploration and exploitation abilities. Elite method based on chaotic local search is used to refine population quality. Comprehensive experimental results indicate the competitive performance of MLBSA. Abstract: Obtaining appropriate parameters of photovoltaic models based on measured current-voltage data is crucial for the evaluation, control, and optimization of photovoltaic systems. Although many techniques have been developed to solve this problem, it is still challenging to identify the model parameters accurately and reliably. To improve parameters identification of different photovoltaic models, a multiple learning backtracking search algorithm (MLBSA) is proposed in this paper. In MLBSA, some individuals learn from the current population information and historical population information simultaneously, which aims to maintain population diversity and enhance the exploration ability. While other individuals learn from the best individual of current population to improve the convergence speed and thus enhance the exploitation ability. In addition, an elite strategy based on chaotic local search is developed to further refine the quality of current population. The proposed MLBSA is employed to solve the parameters identification problems of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. Comprehensive experimental results and analyses demonstrate that MLBSA outperforms other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficiency.