蜂群算法在太阳电池组件参数辨识中的应用
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摘要
针对太阳电池组件参数辨识精确度不高的问题,提出一种采用蜂群算法应用于参数辨识的方法。太阳电池组件模型采用单二极管串并联模型,在确定太阳能组件参数范围后,利用提出的蜂群算法对电池组件参数进行辨识。在蜂群算法中,不同的蜜蜂用不同类型的路径修改自己的位置,从而避免了过早收敛于局部最优解,进行全局搜索最优解。实验结果表明,蜂群优化算法的辨识的均方根差值为0.00241,计算电流(测量的25组电流值)总误差为0.0413,明显优于混沌无性繁殖算法、混沌粒子群算法、模式搜索算法、模拟退化算法,为太阳电池组件的参数辨识提供了一种新的方法。
In view of the low identification precision of the parameters of the solar cell module,this paper presents a method of parameter identification based on the bee colony algorithm. The model of solar cell module is the interconnection of solar cells in series or/and parallel configuration,in which the parameters range of the solar modules are determined,and the parameters of the battery components are identified by the proposed algorithm. In the bee colony algorithm,different bees use different types of paths to modify their position,thus avoiding prematurely convergence to local optimal solution,getting the optimal solution for the global search. Experimental result shows that the root mean square error(RMSE)for the identification on the bee colony optimization algorithm is 0.00241,total current error within0.0413(25 groups measured current),obviously superior to the chaotic asexual propagation algorithm,chaotic particle swarm algorithm,pattern search algorithm,simulated annealing algorithm,providing a new method for parameter identification of solar cell module.
In view of the low identification precision of the parameters of the solar cell module,this paper presents a method of parameter identification based on the bee colony algorithm. The model of solar cell module is the interconnection of solar cells in series or/and parallel configuration,in which the parameters range of the solar modules are determined,and the parameters of the battery components are identified by the proposed algorithm. In the bee colony algorithm,different bees use different types of paths to modify their position,thus avoiding prematurely convergence to local optimal solution,getting the optimal solution for the global search. Experimental result shows that the root mean square error(RMSE)for the identification on the bee colony optimization algorithm is 0.00241,total current error within0.0413(25 groups measured current),obviously superior to the chaotic asexual propagation algorithm,chaotic particle swarm algorithm,pattern search algorithm,simulated annealing algorithm,providing a new method for parameter identification of solar cell module.
引文
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[12]Bouzidi K,Chegaar M,Nehaoua N.New method to extract the parameters of solar cells from their illuminated I-V curve[A].4th International Conference on Computer Integrated Manufacturing[C],Sétif,Algeria,2007.
[2]程泽,董梦男,杨添剀,等.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.[2]Cheng Ze,Dong Mengnan,Yang Tiankai,et al.Extraction of solar cell model parameters based on selfadaptive chaos particle swarm optimization algorithm[J].Transactions of China Electrotechnical Society,2014,29(9):245-252.
[3]简献忠,魏凯,郭强.蜂群算法在光伏电池双二极管五参数模型中的应用[J].光子学报,2015,44(1):1-5.[3]Jian Xianzhong,Wei Kai,Guo Qiang.Artificial bee swarm in the application of photovoltaic cell fiveparameter double-diode model[J].Acta Photonica Sinica,2015,44(1):1-5.
[4]Yuan Xiaofang,He Yuqing,Liu Liangjiang.Parameter extraction of solar cell models using chaotic asexual reproduction optimization[J].Neural Compututing&Application,2015,26:1227-1239.
[5]Askarzadeh A,Rezazadeh A.Artificial bee swarm optimization algorithm for parameters identification of solar cell models[J].Applied Energy,2013,(102):943-949.
[6]Easwarakhanthan T,Bottin J,Bouhouch I,et al.Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers[J].Solar Energy,1986,4(1):1-12.
[7]Huang Wei,Cong Jiang,Xue Lingyun,et al.Extracting solar cell model parameters based on chaos particle swarm algorithm[A].Proceedings of international conference on electric information and control engineering(ICEICE)[C],Huangshi,China,2011.
[8]AlRashidi M R,AlHajri M F,El-Naggar K M,et al.Anew estimation approach for determining the I-Vcharacteristics of solar cells[J].Solar Energy,2011,85(7):1543-1550.
[9]Zagrouba M,Sellami A,Bouaicha M,et al.Identification of PV solar cells and modules parameters using the genetic algorithms:Application to maximum power extraction[J].Solar Energy,2010,84(5):860-866.
[10]Gong Wenyin,Cai Zhihua.Parameter extraction of solar cell models using repaired adaptive differential evolution[J].Solar Energy,2013,94:209-220.
[11]El-Naggar K M,AlRashidi M R,AlHajri M F,et al.Simulated annealing algorithm for photovoltaic parameters identification[J].Solar Energy,2012,86(1):266-274.
[12]Bouzidi K,Chegaar M,Nehaoua N.New method to extract the parameters of solar cells from their illuminated I-V curve[A].4th International Conference on Computer Integrated Manufacturing[C],Sétif,Algeria,2007.