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| Load Modeling Based on Support Vector Machine Based on Bayesian Evidence Framework |
| Wang Zhenshu1, 2, Li Linchuan1, Niu Li3 |
| 1. Key Laboratory of Power System Simulation and Control of Ministry of Education Tianjin University Tianjin 300072 China 2. Shandong University Jinan 250061 China 3. State Nuclear Electric Power Design & Research Institute Beijing 100032 China |
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Abstract Load modeling is still one of the difficult problems in power system. Accurate load model plays a very important role in power system digital simulation. This paper presents a support vector machine (SVM) load modeling method which bases on Bayesian evidence framework. According to the load characteristic data acquired from wide area measurement system(WAMS), the load model based on SVM method is founded, and it chooses Gaussian radial basis function (RBF) to optimize the structure of the model. Among three levels of Bayesian evidence framework inference, the level 1inference is used to explain SVM training, both levels 2 and 3 can also be applied to SVM. Load model parameters are identified and optimized by using three inference levels of Bayesian evidence framework. Simulation tests for SVM load model verify the validity of this method. SVM load model based on Bayesian evidence framework has good generalization ability, flexible structure, and rapid calculation speed, so it can describe the actual load characteristics more accurately.
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Received: 03 February 2009
Published: 17 February 2014
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[1] 贺仁睦. 电力系统精确仿真与负荷模型实用化[J].电力系统自动化, 2004, 28(16):4-7.
[2] 张红斌, 贺仁睦, 刘应梅. 基于KOHONEN神经网络的电力系统负荷动特性聚类与综合[J]. 中国电机工程学报, 2003, 23(5): 1-6.
work [J]. Proceedings of the CSEE, 2003, 23(5): 1-6.
[3] Dmitry N Kosterev, Carson W Taylor, William A Mittelstadt. Model validation for the August 10, 1996 WSCC system outage[J]. IEEE Transactions on Power Systems, 1999, 14(3): 967-979.
[4] 李培强, 李欣然, 陈辉华, 等. 基于减法聚类的模糊神经网络负荷建模[J]. 电工技术学报, 2006, 21 (9): 2-6.
[5] 鞠平, 谢会玲, 陈谦. 电力负荷建模研究的发展趋势[J]. 电力系统自动化, 2007, 31(2): 1-4.
[6] 许树楷, 谢小荣, 辛耀中. 基于同步相量测量技术的广域测量系统应用现状及发展前景[J]. 电网技术, 2005, 29(2): 44-49.
[7] 宋晓娜, 毕天姝, 吴京涛, 等. 基于WAMS的电网扰动识别方法[J]. 电力系统自动化, 2006, 30(5): 24- 28.
[8] 王振树, 张波, 李欣唐. 新型电力故障录波监测系统[J]. 电力系统自动化, 2007, 31(10): 92-96.
[9] Vapnik V. The nature of statistical learning theory [M]. New York: Springer Verlag, 2001.
[10] Cortes C, Vapnik V. Support vector networks [J]. Machine Learning(S0885-6125), 1995(20): 273-297.
[11] 邓乃扬, 田英杰. 数据挖掘中的新方法——支持向量机[M]. 北京: 科学技术出版社, 2004.
[12] 荣海娜, 张葛祥, 金炜东. 系统辨识中支持向量机核函数及其参数的研究[J]. 系统仿真学报, 2006, 18(11): 3204-3208.
[13] 郑小霞, 钱锋. 基于支持向量机的在线建模方法及应用[J]. 信息与控制, 2005, 34(5): 636-640.
[14] 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.
[15] Byoung-Kon C, Hsiao-Dong C, Yinhong L, et al. Measurement-based dynamic load models: derivation, comparison, and validation[J]. IEEE Transactions on Power Systems, 2006, 21(3): 1276-1283.
[16] Maitra A, Gaikwad A, Zhang P, et al. Using system disturbance measurement data to develop improved load models[C]. IEEE Power Systems Conference and Exposition, 2006: 1978-1985.
[17] 张明光, 阎威武, 李战明. 基于支持向量机的非线性系统辨识研究[J]. 计算机应用研究, 2006(5): 47-48.
[18] 郝文斌, 李群湛, 等. 基于支持向量机的变压器励磁涌流仿真实现[J]. 电网技术, 2006, 30(1): 60-64.
[19] Mackay D J C. Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks[J]. Network: Computation in Neural Systems, 1995, 6(3): 469-505.
[20] Kowk J T. The evidence framework applied to support vector machine[J]. IEEE Trans on Neural Network, 2000, 11(5): 1162-1173.
[21] 阎威武, 常俊林, 邵惠鹤. 一种贝叶斯证据框架下
支持向量机建模方法的研究[J]. 控制与决策, 2004, 19(5): 525-528. |
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2009, Vol. 24 
