Parameter identification of photovoltaic cell model based on improved ant lion optimizer. (1st November 2017)

• Record Type: Journal Article
• Authors: Wu, Zhongqiang; Yu, Danqi; Kang, Xiaohua
• Is Part Of: Energy conversion and management. Volume 151(2017)
• Page Start: 107
• Page End: 115
• Publication Date: 2017-11-01
• Subjects: Ant lion optimizer -- Lambert W function -- Parameter identification -- Photovoltaics -- Optimization; 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.2017.08.088
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: The uniformity and ergodicity of population in IALO algorithm is improved. The local and global searching capability of IALO algorithm is improved. The search range is decreased and the time of optimization is short. Abstract: For photovoltaic cell model, the accurate identification of parameters has a great impact on the prediction of power and the maximum power point tracking, so it always has a high demand on accuracy. Many intelligent algorithms can identify the parameters of the model, but there are the situations that the convergence speed is slow, influenced by the initial value, and easy to premature convergence. Ant lion optimizer (ALO) is a novel intelligent algorithm proposed in recent years, and it also has the problems mentioned above. The improved ant lion optimizer (IALO) arranges the initial positions of individuals by chaotic sequence to enhance the uniformity and ergodicity of population; The idea of particle swarm algorithm is introduced in the position updating of individuals, and the position of individuals are calculated based on the current best individuals and the global best individual to enhance the local and global searching capability; The dynamic contraction region in which the best individual is considered is used to decrease the search range and shorten the time of optimization efficiently. Comparing with particle swarm algorithm, bat algorithm and ant lion algorithm in the simulation, IALO algorithm is better than the other algorithm for four standard test functions. IALO algorithm is also used to identify the parameter of photovoltaic cell. The results show that, for I ph the average of IALO algorithm is 5.180, the average of ALO algorithm is 5.179, and the average of PSO algorithm is 5.052. For I o the average of IALO algorithm is 1.02, the average of ALO algorithm is 0.97, and the average of PSO algorithm is 0.87. For A the average of IALO algorithm is 48.0, the average of ALO algorithm is 37.4, and the average of PSO algorithm is 29.7. For R s the average of IALO algorithm is 0.146, the average of ALO algorithm is 0.140, and the average of PSO algorithm is 0.142. For R sh the average of IALO algorithm is 298.6, the average of ALO algorithm is 221.5, and the average of PSO algorithm is 188.9. So IALO algorithm is the best.