基于自适应混沌粒子群算法的光伏电池模型参数辨识

Extraction of Solar Cell Model Parameters Based on Self-Adaptive Chaos Particle Swarm Optimization Algorithm

光伏电池模型参数的快速准确辨识在光伏阵列的输出功率预测、最大功率点跟踪以及电池故障模型的特性研究方面具有非常重要的工程意义。针对大部分传统智能算法用于系统参数辨识时的辨识精确受参数初值影响较大,而且算法易陷入早熟的问题,利用自适应混沌粒子群算法(SA-CPSO)对光伏电池模型参数进行辨识。将混沌算法与粒子群算法融合,对粒子群进行混沌初始化并促使陷入局部最优的粒子进行混沌搜索,引导其跳出局部极值从而搜索到更好的解;同时引入自适应调整策略来有效控制全局与局部搜索,提高了进化后期算法的收敛精度。经过仿真和实验测试,证明SA-CPSO算法在光伏电池模型参数辨识方面具有较高的精确度和快速性。还通过实验探讨了辐照度变化对太阳能电池参数的影响。

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.