一种基于遗传算法的光伏阵列参数辨识方法
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摘要
提出一种基于遗传算法的参数辨识方法,在保证辨识精度的前提下,将光伏电池参数辨识后得到的多组结果收敛为一组参数值。依据光伏电池的I-U方程和参数特性,基于实测数据,通过参数辨识方法确定待求解参数的可行解范围;利用基本遗传算法辨识待求解的参数值,得到一组准确的辨识值;当出现部分数据辨识结果的误差较大时,利用一种基于混合遗传算法的参数辨识方法,提高这部分数据辨识结果的精度。基于实测数据,利用所提出的辨识方法确定参数值,通过预测结果与实测结果的比较,验证了该方法的准确性与实用性。
A parameter identification method is presented based on genetic algorithm, which condenses multiple identification results of photovoltaic cell parameter into a set of parameter values with high identification precision. Firstly, based on the I-U equation and parameter property, the range of feasible solution can be obtained by utilizing the measured data with parameter identification method.Then, basic genetic algorithm is used to identify values of the unknown parameters, a set of accurate identification results are gotten; and a new method based upon hybrid genetic algorithm is proposed to improve accuracy of those values that exist large error in some identification results. Finally, based on the measured data, value of the parameters can be determined by the method in this paper. Comparing predicted results with measured results, the accuracy and practicality of the proposed method are verified.
A parameter identification method is presented based on genetic algorithm, which condenses multiple identification results of photovoltaic cell parameter into a set of parameter values with high identification precision. Firstly, based on the I-U equation and parameter property, the range of feasible solution can be obtained by utilizing the measured data with parameter identification method.Then, basic genetic algorithm is used to identify values of the unknown parameters, a set of accurate identification results are gotten; and a new method based upon hybrid genetic algorithm is proposed to improve accuracy of those values that exist large error in some identification results. Finally, based on the measured data, value of the parameters can be determined by the method in this paper. Comparing predicted results with measured results, the accuracy and practicality of the proposed method are verified.
引文
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[14]王宏刚,曾建潮.基于Metropolis判别准则的遗传算法[J].控制与决策,1998,13(2):181-184.Wang Honggang,Zen Jianchao.Genetic algorithm based on the metropolis criteria[J].Control And Decision,1998,13(2):181-184.
[2]Jain A,Gow,C D Manning.Devel opment of a photovoltaic array model for use in power electronic simulation studies[J].IEE Proc Electric Power Appl,1999,146(2):193-200.
[3]周建良,王冰,张一鸣.基于实测数据的光伏阵列参数辨识与输出功率预测[J].可再生能源,2013,30(7):1-4.Zhou Jianliang,Wang Bing,Zhang Yiming.Parameter identification and output power prediction of photovoltaic array based on the measured data[J].Renewable Energy Resources,2013,30(7):1-4.
[4]鲁延京,陈英武,杨志伟.求解约束优化问题的粒子进化变异遗传算法[J].控制与决策,2012,27(10):1441-1446.Lu Yanjing,Chen Yingwu,Yang Zhiwei.Improved GA with particle swarm's evolutionary strategy for solving constrained optimization problems[J].Control and Decision,2012,27(10):1441-1446.
[5]Messenger R A,Ventre J.Photovoltaic Systems Engineering(second ed)[M].Boca Raton,FL:CRC Press LLC,2004.
[6]Dieter K Schroder.Semiconductor Material and Device Characterization(second ed)[M].New York:John Wiley&Sons Inc,1998.
[7]Virtuani A,Lotter E,Powalla M.Performance of Cu(In,Ga)Se2 solar cells under low irradiance[J].Thin Solid Films,2003,431(1):443-447.
[8]黄存荣,王冰,程永乐,等.光伏电池参数求解算法与最大功率点基准值的研究[J].可再生能源,2013,31(6):26-29.Huang Cunrong,Wang Bing,Cheng Yongle,et al.Research on parameter solution and reference value at maximum power point of photovoltaic cell[J].Renewable Energy Resources,2013,31(6):26-29.
[9]高金辉,苏军英,李迎迎.太阳电池模型参数求解算法的研究[J].太阳能学报,2012,33(9):1458-1462.Gao Jinhui,Su Junying,Li Yingying.Research on the solving algorithm of solar cell model's parameters[J].Acta Engrgiae Solaris Sinica,2012,33(9):1458-1462.
[10]莫鸿强,罗飞,毛宗源.遗传算法的全局快速寻优[J].控制理论与应用,2002,19(5):809-811.Mo Hongqiang,Luo Fei,Mao Zongyuan.On global rapid optimum seeking of GAs[J].Control Theory And Applications,2012,19(5):809-811.
[11]陈华根,吴健生,王家林.模拟退火算法机理研究[J].同济大学学报(自然科学版),2004,32(6):802-805.Chen Huagen,Wu Jiansheng,Wang Jialin.Mechanism study of simulated annealing algorithm[J].Journal of TongJi University(Natural Science),2004,32(6):802-805.
[12]高海昌,冯博琴,朱利.智能优化算法求解TSP问题[J].控制与决策,2006,21(3):241-247.Gao Haichang,Feng Boqin,Zhu Li.Reviews of the metaheuristic algorithm for TSP[J].Control And Decision,2006,21(3):241-247.
[13]潘伟,刁华宗,井元伟.一种改进的实数自适应遗传算法[J].控制与决策,2006,21(7):792-795.Pan Wei,Diao Huazong,Jing Yuanwei.An improved real-value adaptive genetic algorithm[J].Control And Decision,2006,21(7):792-795.
[14]王宏刚,曾建潮.基于Metropolis判别准则的遗传算法[J].控制与决策,1998,13(2):181-184.Wang Honggang,Zen Jianchao.Genetic algorithm based on the metropolis criteria[J].Control And Decision,1998,13(2):181-184.