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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.
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Keywords:
- solar cell temperature /
- thermal model /
- support vector machines /
- particle swarm optimization
[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 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 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 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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[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 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 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 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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