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基于主成分分析和多分类相关向量机的GIS局部放电模式识别

, PP. 225-231

Keywords: 气体绝缘组合电器,局部放电,主成分分析,多分类相关向量机,模式识别

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Abstract:

GIS局部放电模式识别是其状态评估的重要部分,搭建了252kVGIS局部放电超高频检测仿真实验平台,模拟了4种典型的GIS局部放电模型,并通过试验建立了相应的超高频信号图谱数据库,然后根据信号特点提取了26个原始特征量;采用主成分分析法对特征空间进行降维处理,最终得到10个新的特征量,将原始特征量和降维后的特征量分别输入到多分类相关向量机(M-RVM)中进行分析,结果表明,以降维后的特征量作为输入量,其识别率要高于降维前的;并且采用BN、SVM和M-RVM三种分类器进行对比分析,结果表明,无论是采用原始特征参量还是降维后的参量作为输入量,M-RVM方法的识别率都是最高,其中降维后的识别率大于85%。

References

[1]  司文荣, 李军浩, 袁鹏, 等. 基于波形非线性映射的多局部放电脉冲群快速分类[J]. 电工技术学报, 2009, 24(3): 217-228. Si Wenrong, Li Junhao, Yuan Peng, et al. The fast grouping technique of PD sequence based on the nonlinear mapping of pulse shapes[J]. Transactions of China Electrotechnical Society, 2009, 24(3): 217- 228.
[2]  李信, 李成榕, 丁立健, 等. 基于特高频信号检测GIS局放模式识别[J]. 高电压技术, 2003, 14(3): 16-20. Li Xin, Li Chengrong, Ding Lijian, et al. Identifica- tion of PD patterns in gas insulated swichgear(GIS) based on UHF signals[J]. High Voltage Engineering, 2003, 14(3): 16-20.
[3]  唐炬, 周倩, 许中荣, 等. GIS特高频局放信号的数学建模[J]. 中国电机工程学报, 2005, 25(19): 106- 110. Tang Ju, Zhou Qian, Xu Zhongrong, et al. Establish- ment of mathematical model for partial discharge in GIS using UHF method[J]. Proceedings of the CSEE, 2005, 25(19): 106-110.
[4]  Judd M D, Cleary G P, Bennoch C J. Applying UHF partialdischarge detection to power transformers[J]. IEEE Power Engineering Review, 2002, 22(8): 57-59.
[5]  司文荣, 李军浩, 袁鹏, 等. 气体绝缘组合电器多局部放电源的检测与识别[J]. 中国电机工程学报, 2009, 29(16): 119-125. Si Wenrong, Li Junhao, Yuan Peng, et al. Detection and identification techniques for multi-PD sources in GIS[J]. Proceedings of the CSEE, 2009, 29(16): 119-125.
[6]  胡文堂, 高胜友, 余绍峰, 等. 统计参数在变压器局部放电模式识别中的应用[J]. 高电压技术, 2009, 35(2): 277-281. Hu Wentang, Gao Shengyou, Yu Shaofeng, et al. Application of statistic parameters in recognition of partial discharge in transformers[J]. High Voltage Engineering, 2009, 35(2): 277-281.
[7]  Gulski E, Kreuger F H. Computer-aided recognition of discharge sources[J]. IEEE Transactions on Electrical Insulation, 1992, 27(1): 82-92.
[8]  Mazroua A A. Discrimination between PD pulse shapes using different neural network paradigms[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1994, 1(6): 1119-1131.
[9]  张晓虹, 张亮, 乐波, 等. 基于局部放电的矩特征分析大电机主绝缘的老化[J]. 中国电机工程学报, 2002, 22(5): 94-98. Zhang Xiaohong, Zhang Liang, Le Bo, et al. Analysis onaging condition of stator winding insulation of generator basedon the moment characteristics of partial discharge[J]. Proceedings of the CSEE, 2002, 22(5): 94-98.
[10]  廖瑞金, 杨丽君, 孙才新, 等. 基于局部放电主成分因子向量的油纸绝缘老化状态统计分析[J]. 中国电机工程学报, 2006, 26(14): 114-119. Liao Ruijin, Yang Lijun, Sun Caixin, et al. Aging conditionassessment of oil-paper based on principal component and fac-tor analysis of partial discharge[J]. Proceedings of the CSEE, 2006, 26(14): 114-119.
[11]  Damoulas T, Girolami M A. Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection[J]. Bioinformatics, 2008, 24(10): 1264-1270.
[12]  Girolami M, Rogers S. Hierarchic Bayesian models for kernel learning[C]. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 2005, 241-248.
[13]  李信, 李成榕, 丁立健, 等. 基于特高频信号检测GIS局放模式识别[J]. 高电压技术, 2003, 14(3): 16-20. Li Xin, Li Chengrong, Ding Lijian, et al. Identifica- tion of PD patterns in gas insulated swichgear(GIS) based on UHF signals[J]. High Voltage Engineering, 2003, 14(3): 16-20.
[14]  Psorakis I, Damoulas T, Girolami M A. Multiclass relevance vector machines: sparsity and accuracy[J]. IEEE Transactions on Neural Networks, 2010, 21(10): 1588-1598.
[15]  Damoulas T, Girolami M A. Combining feature spaces for classification[J]. Pattern Recognition, 2009, 42(11): 2671-2683.

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