Abstract：Extracting solar cell model parameters with accuracy and rapidity is very important for forecast of power generation of photovoltaic arrays, maximum power point tracking(MPPT) and characteristics study of solar cell fault model. According to the accuracy of parameter estimation using most traditional intelligent algorithms has strong relevance with the initialized value of parameters, moreover, these algorithms almost have a defect of easily falling into local optimum. The paper puts forward a new method to extract the parameters of solar cells based on self-adaptive chaos particle swarm optimization algorithm(SA-CPSO). This paper introduces chaos algorithm into the particle algorithm for chaos initialization of particles and bring chaos perturbation to particles which fall into local optimization which making these particles jump out of local optimum condition so as to achieve the global optimization. At the same time, in order to enhance the balance of the global optimal search and the local search of the particle swarm algorithm, this paper combines the self-adaptive algorithm with the particle swarm algorithm to improve the accuracy in the later evolution period. The results of the simulation experiments show that the self-adaptive chaos particle swarm optimization algorithm has great advantages of convergence accuracy and rapidity to extract the parameters of solar cell. Besides, this paper also analyzes the influence of the irradiance changes on the parameters of solar cell model.
程泽, 董梦男, 杨添剀, 韩丽洁. 基于自适应混沌粒子群算法的光伏电池模型参数辨识[J]. 电工技术学报, 2014, 29(9): 245-252.
Cheng Ze, Dong Mengnan, Yang Tiankai, Han Lijie. Extraction of Solar Cell Model Parameters Based on Self-Adaptive Chaos Particle Swarm Optimization Algorithm. Transactions of China Electrotechnical Society, 2014, 29(9): 245-252.
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