|
|
|
| Transformer Winding Hot-Spot Temperature Prediction Model of Support Vector Machine Optimized by Genetic Algorithm |
| Chen Weigen1, Teng Li1, 2, Liu Jun1, Peng Shangyi1, Sun Caixin1 |
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology
Chongqing University Chongqing 400030 China;
2. Shiqu Power Supply Branch of Chongqing Electric Power Company Chongqing 400015 China |
|
|
|
|
Abstract The operation life and the load capacity of the oil-immersed power transformers are closely related with the winding hot-spot temperature(HST). Accurate prediction of the transformer winding hot-spot temperature is one of the key technologies of effectively preventing the thermal fault, accurately predicting the operation life of the transformer and optimizing design of transformer. This paper studies the support vector machine(SVM) modeling of the winding hot-spot temperature. In order to improve the accuracy of model predictions, and the RBF kernel function is selected to optimize the model structure; and optimized the parameters by the genetic algorithm(GA). Combined with the measured temperature of the temperature-rise transformer windings simulated in the laboratory, the characteristic quantities are employed as inputs, and the HST is used as output of the SVM model, and the measured temperature are divided into training set and prediction set. The transformer winding hot-spot temperature prediction model of support vector machine optimized by genetic algorithm is built. The experiment show that, the prediction results of the GA-SVM model are basically identical with the measured temperature, and are better than the prediction results of BP neural network and Elman neural network.
|
|
Received: 11 April 2012
Published: 16 June 2014
|
|
|
|
|
|
[1] 熊浩, 陈伟根, 杜林, 等. 基于T-S模型的电力变压器顶层油温预测研究[J]. 中国电机工程学报, 2007, 27(30): 15-19.
Xiong Hao, Chen Weigen, Du Lin, et al. Study on prediction of top-oil temperature for power transformer based on T-S model[J]. Proceedings of the CSEE, 2007, 27(30): 15-19.
[2] Tang W H, Wu Q H, Richardson Z J. A simplified transformer thermal model based on thermal-electric analogy[J]. IEEE Transactions on Power Delivery, 2004, 19(3): 1112-1119.
[3] Chen W G, Pan C, Yun Y X. Power transformer top-oil temperature model based on thermal-electric analogy theory[J]. European Transactions on Electrical Power, 2009, 19(3): 341-354.
[4] 滕黎, 陈伟根, 孙才新. 油浸式电力变压器动态热路改进模型[J]. 电网技术, 2012, 36(4): 236-241.
Teng Li, Chen Weigen, Sun Caixin. An improved dynamic thermal circuit model of oil-immersed power transformer[J]. Power System Technology, 2012, 36(4): 236-241.
[5] IEEE. IEEE Std C57. 91-1995 IEEE guide for loading mineral-oil-immersed transformers[S]. Piscataway, NJ, USA: the Institute of Electrical and Electronics Engineers, 1995.
[6] 中国国家标准化管理委员会. GB/T 15164—1994油浸式电力变压器负载导则[S]. 北京: 中国标准出版社, 1994.
[7] Lesieutre B C, Hagman W H, Kirtley J L. An improved transformer top oil temperature model for use in an on-line monitoring and diagnostic system[J]. IEEE Transactions on Power Delivery, 1997, 12(1): 249-256.
[8] Swift G, Molinski T S, Lehn W. A fundamental approach to transformer thermal modeling - part I: theory and equivalent circuit[J]. IEEE Transactions on Power Delivery, 2001, 16(2): 171-175.
[9] Susa D, Lehtonen M, Nordman H. Dynamic thermal modelling of power transformers[J]. IEEE Transac- tions on Power Delivery, 2005, 20(1): 197-204.
[10] Pradhan M K, Ramu T S. Prediction of hottest spot temperature(HST) in power and station transfor- mers[J]. IEEE Transactions on Power Delivery, 2003, 18(4): 1275-1283.
[11] Galdi V, Ippolito L, Piccolo A, et al. Neural diagnostic system for transformer thermal overload protection[J]. IEE Proceedings on Electric Power Applications, 2000, 147(5): 415-421.
[12] He Q, Si J N, Tylavsky D J. Prediction of top-oil temperature for transformers using neural networks[J]. IEEE Transactions on Power Delivery, 2000, 15(4): 1205-1211.
[13] Assuncao T, Silvino J L, Resende P. Transformer top-oil temperature modeling and simulation[J]. Transactions on Engineering, Computing and Tech- nology, 2006, 15(10): 240-245.
[14] Vapnik V N. The nature of statistical learning theory[M]. New York: Springer, 2000.
[15] 董明, 孟源源, 徐长响, 等. 基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究[J]. 中国电机工程学报, 2003, 23(7): 88-92.
Dong Ming, Meng Yuanyuan, Xu Changxiang, et al. Fault diagnosis model for power transformer based on support vector machine and dissolved gas analysis[J]. Proceedings of the CSEE, 2003, 23(7): 88-92.
[16] 牛东晓, 刘达, 陈广娟, 等. 基于遗传优化的支持向量机小时负荷滚动预测[J]. 电工技术学报, 2007, 22(6): 148-153.
Niu Dongxiao, Liu Da, Chen Guangjuan, et al. Support vector machine models optimized by genetic algorithm for hourly load rolling forecasting[J]. Transactions of China Electrotechnical Society, 2007, 22(6): 148-153.
[17] 张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-42.
Zhang Xuegong. Introduction to statistical learning theory and support vector machines[J]. Acta Automatica Sinica, 2000, 26(1): 32-42.
[18] Keerthi S S, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel[J]. Neural Computation, 2003, 15(7): 1667-1689.
[19] 王群京, 鲍晓华, 倪有源, 等. 基于支持向量机和遗传算法的爪极发电机建模及参数优化[J]. 电工技术学报, 2006, 21(4): 57-61.
Wang Qunjing, Bao Xiaohua, Ni Youyuan, et al. Modeling and parameter optimization of the claw-pole alternator based on support vector machines and genetic algorithms[J]. Transactions of China Electrotechnical Society, 2006, 21(4): 57-61.
[20] 王春林, 周昊, 李国能, 等. 基于支持向量机与遗传算法的灰熔点预测[J]. 中国电机工程学报, 2007, 27(8): 11-15.
Wang Chunlin, Zhou hao, Li Guoneng, et al. Combining support vector machine and genetic algorithm to predict ash fusion temperature[J]. Proceedings of the CSEE, 2007, 27(8): 11-15.
[21] Nguyen H, Baxter G W, Reznik L. Soft computing techniques to model the top-oil temperature of power transformers[C]. International Conference on Intelligent Systems Applications to Power Systems (ISAP), Taiwan, 2007: 1-6. |
|
|
|
2014, Vol. 29 
