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基于粒子群优化支持向量机的太阳电池温度预测

赵志刚 张纯杰 苟向锋 桑虎堂

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基于粒子群优化支持向量机的太阳电池温度预测

赵志刚, 张纯杰, 苟向锋, 桑虎堂

Solar cell temperature prediction model of support vector machine optimized by particle swarm optimization algorithm

Zhao Zhi-Gang, Zhang Chun-Jie, Gou Xiang-Feng, Sang Hu-Tang
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  • 建立通用而精确的太阳电池热模型对光伏系统的建模、输出功率与转换效率的损失分析至关重要. 基于复杂的太阳电池温度机理, 分别研究了太阳电池温度的稳态热模型(steady state thermal model, SSTM)和支持向量机(support vector machines, SVM) 方法建立的精确预测热模型. 首先, 基于空气温度、太阳辐射强度、风速3个最主要因素与太阳电池温度的近似线性关系, 在已有SSTM的基础上, 建立并校正了太阳电池的SSTM并采用差分进化算法提取模型的未知参数. 其次, 为提高SVM的模型预测精度, 采用粒子群优化(particle swarm optimization, PSO) 算法对SVM的核参数和惩罚因子进行动态寻优, 在确定输入/输出样本集并划分训练集和测试集的基础上, 建立了基于粒子群优化支持向量机(PSO-SVM)的太阳电池温度精确预测热模型. 最后, 搭建实验平台, 在实验操作过程中减弱空气湿度、太阳入射角和热迟滞效应等因素对太阳电池温度的耦合. 通过实验对比表明, 建立的预测热模型性能可靠、全面、简洁, 其参数寻优算法优于遗传算法和交叉校验法, 模型预测精度优于反向传播神经网络(back propagation neural network) 和SSTM.
    Establishing a general and precise solar cell temperature model is of crucial importance for photovoltaic system modeling, the loss analysis of output power, and conversion efficiency. According to the complex mechanism of solar cell temperature, in this paper we study the steady state thermal model (SSTM) of solar cell temperature and accurate prediction model of method of support vector machine (SVM). Firstly, based on the approximate linear relationship among air temperature, solar radiation intensity, wind speed and solar cell temperature, the polynomial model of solar cell temperature is established and the unknown parameters of the model are extracted with the improved differential evolution algorithm. Secondly, in order to improve the accuracy of SVM prediction model, the particle swarm optimization algorithm is adopted to optimize the parameters (including kernel parameter g and penalty factor C from the radial basis function kernel) of SVM. After the input/output sample set is determined and the training set and test set are classified, a prediction model of solar cell temperature based on particle swarm optimization support vector machine is established. Finally, experimental acquisition platform is built to reduce the influences of air humidity, solar incidence angle, and thermal hysteresis effects on PV cell temperature. Through contrasting experiments, it is shown that the established fitting of the SSTM is better than the models given in other literature, and the prediction model is reliable, comprehensive and simple. The selected parameter optimization algorithm is superior to genetic algorithm and cross-validation method established on the optimization performance, and the accuracy of prediction model is superior to the prediction performance of back propagation neural network and identified SSTM.
    [1]

    Farivar G, Asaei B, Haghdadi N, Iman-Eini H 2011 2nd Power Electronics, Drive Systems and Technologies Conference Tehran, The Islamic Republic of Iran, February 16-17, 2011 p336

    [2]

    Ju X, Vossier A, Wang Z F, Dollet A, Flamant G 2013 Sol. Energy 93 80

    [3]

    Torres-lobera D, Valkealahti S 2014 Sol. Energy 105 632

    [4]

    Trinuruk P, Sorapipatana C, Chenvidhya D 2009 Renew. Energy 34 2515

    [5]

    Liang Q B, Shu B F, Sun L J, Zhang Q Z, Chen M B 2014 Acta Phys. Sin. 63 168801 (in Chinese) [梁齐兵, 舒碧芬, 孙丽娟, 张奇淄, 陈明彪 2014 物理学报 63 168801]

    [6]

    Hoang P, Bourdin V, Liu Q, Caruso G, Archambault V 2014 Sol. Energy Mater. Sol. Cells 125 325

    [7]

    Górecki K, Górecki P, Paduch K 2014 J. Phys. Conf. Ser. 494 1

    [8]

    Anantha Krishna H, Misra N K, Suresh M S 2011 IEEE Trans. Aerosp. Electron. Syst. 47 782

    [9]

    Torres-Lobera D, Valkealahti S 2013 Sol. Energy 93 183

    [10]

    Ilhan C, Erkaymaz O, Gedik E, Grel A E 2014 Case Studies Therm. Eng. 3 11

    [11]

    Sun Z H, Jiang F 2010 Chin. Phys. B 19 110502

    [12]

    Tang Z J, Ren F, Peng T, Wang W B 2014 Acta Phys. Sin. 63 050505 (in Chinese) [唐舟进, 任峰, 彭涛, 王文博 2014 物理学报 63 050505]

    [13]

    Tian Z D, Gao X W, Shi T 2014 Acta Phys. Sin. 63 160508 (in Chinese) [田中大, 高宪文, 石彤 2014 物理学报 63 160508]

    [14]

    Chen A L, Feng L N, Du C S, Zhang C H 2011 Trans. CES 26 140 (in Chinese) [陈阿莲, 冯丽娜, 杜春水, 张承慧 2011 电工技术学报 26 140]

    [15]

    Chen W G, Teng L, Liu J, Peng S Y, Sun C X 2014 Trans. CES 26 44 (in Chinese) [陈伟根, 滕黎, 刘军, 彭尚怡, 孙才新 2014 电工技术学报 26 44]

    [16]

    Matsukawa H, Koshiishi K, Koizumi H, Kurokawa K, Hamada M, Bo L 2003 Sol. Energy Mater. Sol. Cells 75 537

    [17]

    Wang W J, Men C Q 2014 Support Vector Machine Modeling and Its Application (Beijing: Science Press) p211 (in Chinese) [王文剑, 门昌骞 2014 支持向量机建模及应用(北京: 科学出版社) 第211页]

  • [1]

    Farivar G, Asaei B, Haghdadi N, Iman-Eini H 2011 2nd Power Electronics, Drive Systems and Technologies Conference Tehran, The Islamic Republic of Iran, February 16-17, 2011 p336

    [2]

    Ju X, Vossier A, Wang Z F, Dollet A, Flamant G 2013 Sol. Energy 93 80

    [3]

    Torres-lobera D, Valkealahti S 2014 Sol. Energy 105 632

    [4]

    Trinuruk P, Sorapipatana C, Chenvidhya D 2009 Renew. Energy 34 2515

    [5]

    Liang Q B, Shu B F, Sun L J, Zhang Q Z, Chen M B 2014 Acta Phys. Sin. 63 168801 (in Chinese) [梁齐兵, 舒碧芬, 孙丽娟, 张奇淄, 陈明彪 2014 物理学报 63 168801]

    [6]

    Hoang P, Bourdin V, Liu Q, Caruso G, Archambault V 2014 Sol. Energy Mater. Sol. Cells 125 325

    [7]

    Górecki K, Górecki P, Paduch K 2014 J. Phys. Conf. Ser. 494 1

    [8]

    Anantha Krishna H, Misra N K, Suresh M S 2011 IEEE Trans. Aerosp. Electron. Syst. 47 782

    [9]

    Torres-Lobera D, Valkealahti S 2013 Sol. Energy 93 183

    [10]

    Ilhan C, Erkaymaz O, Gedik E, Grel A E 2014 Case Studies Therm. Eng. 3 11

    [11]

    Sun Z H, Jiang F 2010 Chin. Phys. B 19 110502

    [12]

    Tang Z J, Ren F, Peng T, Wang W B 2014 Acta Phys. Sin. 63 050505 (in Chinese) [唐舟进, 任峰, 彭涛, 王文博 2014 物理学报 63 050505]

    [13]

    Tian Z D, Gao X W, Shi T 2014 Acta Phys. Sin. 63 160508 (in Chinese) [田中大, 高宪文, 石彤 2014 物理学报 63 160508]

    [14]

    Chen A L, Feng L N, Du C S, Zhang C H 2011 Trans. CES 26 140 (in Chinese) [陈阿莲, 冯丽娜, 杜春水, 张承慧 2011 电工技术学报 26 140]

    [15]

    Chen W G, Teng L, Liu J, Peng S Y, Sun C X 2014 Trans. CES 26 44 (in Chinese) [陈伟根, 滕黎, 刘军, 彭尚怡, 孙才新 2014 电工技术学报 26 44]

    [16]

    Matsukawa H, Koshiishi K, Koizumi H, Kurokawa K, Hamada M, Bo L 2003 Sol. Energy Mater. Sol. Cells 75 537

    [17]

    Wang W J, Men C Q 2014 Support Vector Machine Modeling and Its Application (Beijing: Science Press) p211 (in Chinese) [王文剑, 门昌骞 2014 支持向量机建模及应用(北京: 科学出版社) 第211页]

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出版历程
  • 收稿日期:  2014-10-31
  • 修回日期:  2014-11-25
  • 刊出日期:  2015-04-05

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