Backtracking search algorithm with Lévy flight for estimating parameters of photovoltaic models. (15th March 2020)

• Record Type: Journal Article
• Authors: Zhang, Yiying; Jin, Zhigang; Zhao, Xiaofang; Yang, Qiuling
• Is Part Of: Energy conversion and management. Volume 208(2020)
• Publication Date: 2020-03-15
• Subjects: Backtracking search algorithm -- Metaheuristic method -- Photovoltaic modeling -- Lévy flight; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.112615
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: The following picture is the flow chart of the proposed backtracking search algorithm with Lévy flight. As can be seen from this picture, the proposed algorithm has a very simple structure. Experimental results demonstrate the proposed algorithm is a simple but significant improvement for the parameters identification of the photovoltaic models. The flow chart of the proposed backtracking search algorithm with Lévy flight. Highlights: Backtracking search algorithm with Lévy flight is proposed. An information sharing mechanism based on Lévy flight is built. A new mutation operator based on hunting mechanism of grey wolves is introduced. The proposed method is used to parameters identification of photovoltaic models. The proposed method is compared with state-of-the-art optimization algorithms. Abstract: An accurate mathematical model plays an important role for simulation, evaluation and optimization of photovoltaic (PV) models. The characteristic current equations describing the PV models are implicit, nonlinear and transcendental. Given the features of the characteristic current equations, traditional optimization algorithms are usually easy to converge to local optimal solutions. Thus using metaheuristic methods called modern optimization algorithms to estimate parameters of PV models has been a research hotspot in recent years. Although many metaheuristic methods have been employed to solve this problem, it is still necessary for researchers to propose new optimization algorithms to obtain more accuracy and reliability solutions. This paper presents a new metaheuristic algorithm called backtracking search algorithm with Lévy flight (LFBSA) to estimate the parameters of PV models. Compared with the basic backtracking search algorithm (BSA), LFBSA has the following two remarkable features. Firstly, an information sharing mechanism with Lévy flight is built to enhance population diversity. Secondly, mutation operator based on the hunting mechanism of grey wolves is introduced to increase the chance of LFBSA to escape from local minima. LFBSA is used to estimate parameters of three different PV models. Experimental results show the proposed LFBSA is superior to BSA and the other compared algorithms in terms of accuracy and reliability.