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电网技术  2011 

采用分形和支持向量机的气体绝缘组合电器局部放电类型识别

, PP. 135-139

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Abstract:

局部放电可以反映气体绝缘组合电器(gasinsulatedswitchgear,GIS)内部的绝缘缺陷,正确识别GIS的放电类型具有重要意义。放电信号特征量的提取和模式识别器的设计对最终判别结果影响较大,因此有必要将分形理论和支持向量机应用到局部放电类型识别中以提高识别效果。在简单介绍了分形理论和支持向量机后,采集了4种绝缘缺陷的放电数据,应用分形理论从j-q-n灰度图中提取放电特征,并构造6个二分类支持向量机识别器,采取投票法识别放电类型。实验结果表明,该方法与反向传播网络方法相比具有识别率高、稳定性好的优点,能有效识别GIS放电类型。

References

[1]  侍海军,孔闻宇.气体绝缘组合电器绝缘现场试验[J].江苏电机工程,2005,24(2):82-84.
[2]  Shi Haijun,Kong Wenyu.Analysis of gas-insulated switchgear field test methods[J].Jiangsu Electrical Engineering,2005,24(2):82-84(in Chinese).
[3]  唐炬,王静,李剑,等.统计参数用于局部放电模式识别的研究[J].高电压技术,2002,28(8):4-6.
[4]  杨健雄,罗国敏,何正友.基于小波熵权和支持向量机的高压输电线路故障分类方法[J].电网技术,2007,31(33):22-26.
[5]  Yang Jianwei,Luo Guomin,He Zhengyou.High voltage transmission line fault classification based on entropy weight of wavelet and support vector machines[J].Power System Technology,2007,31(33):22-26(in Chinese).
[6]  胡清,王荣杰,詹宜巨.基于支持向量机的电力电子电路故障诊断技术[J].中国电机工程学报,2008,28(12):107-111.
[7]  Hu Qing,Wang Rongjie,Zhan Yiju.Fault diagnosis technology based on SVM in power electronics circuit[J].Proceedings of the CSEE,2008,28(12):107-111(in Chinese).
[8]  占勇,程浩忠,丁屹峰,等.基于S变换的电能质量扰动支持向量机分类识别[J].中国电机工程学报,2005,25(4):51-56.
[9]  Zhan Yong,Cheng Haozhong,Ding Yifeng,et al.S-transform-based classification of power quality disturbance signals by support vector machines[J].Proceedings of the CSEE,2005,25(4):51-56(in Chinese).
[10]  刘立柱.概率与模糊信息论及其应用[M].北京:国防工业出版社,2004:10-15.
[11]  温熙森.模式识别与状态监控[M].北京:科学出版社,2007:323-330.
[12]  孙才新,许高峰,唐炬,等.以盒维数和信息维数为识别特征量的GIS局部放电模式识别方法[J].中国电机工程学报,2005,25(3):100-104.
[13]  Sun Caixin,Xu Gaofeng,Tang Ju,et al.PD pattern recognition method using box dimension and information dimension as discriminating features in GIS[J].Proceedings of the CSEE,2005,25(3):100-104 (in Chinese).
[14]  许高峰.全封闭组合电器局部放电信号内置传感检测和分形特征提取的研究[D].重庆:重庆大学,2003.
[15]  Hsu Chihwei,Chang Chihchung,Lin Chihjen.A practical guide to support vector classification[R].Taipei:National Taiwan University,2003.
[16]  成永红,谢小军,陈玉,等.气体绝缘系统中典型缺陷的超宽频带放电信号的分形分析[J].中国电机工程学报,2004,24(8):99-102.
[17]  Cheng Yonghong,Xie Xiaojun,Chen Yu,et al.Study on the fractal characteristics of ultra-wideband partial discharge in gas-insulated system (GIS) with typical defects[J].Proceedings of the CSEE,2004,24(8):99-102 (in Chinese).
[18]  蔡国伟,杜毅,李春山,等.基于支持向量机的中长期日负荷曲线预测[J].电网技术,2006,30(23):56-60.
[19]  Cai Guowei,Du Yi,Li Chunshan,et al.Middle and long-term daily load curve forecasting based on support vector machines[J].Power System Technology,2006,30(23):55-60(in Chinese).
[20]  徐玉秀,张剑,候荣涛.机械系统动力学分形特征及故障诊断方法[M].北京:国防工业出版社,2006:22-24.
[21]  周建萍,郑应平,王志萍.基于Morlet小波核多类支持向量机的故障诊断[J].华东电力,2008,36(8):76-79.
[22]  Zhou Jianping,Zheng Yingping,Wang Zhiping.Fault diagnosis based on Morlet wavelet kernel multi-class support vector machine[J].East China Electric Power,2008,36(8):76-79(in Chinese).
[23]  胡国胜.支持向量机及在电力系统中的应用[J].高电压技术,2007,33(4):101-105.
[24]  Hu Guosheng.Study on support vector machines algorithms and its application to power systems[J].High Voltage Engineering,2007,33(4):101-105(in Chinese).
[25]  葛哲学,孙志强.神经网络与Matlab R2007实现[M].北京:电子工业出版社,2007:108-115.
[26]  Tang Ju,Wang Jing,Li Jian,et al.Statistical parameter method for PD pattern recognition[J].High Voltage Engineering,2002,28(8):4-6(in Chinese).
[27]  王猛,谈克雄,高文胜.局部放电脉冲波形的自回归模型参数识别法[J].高电压技术,2001,27(3):1-3.
[28]  Wang Meng,Tan Kexiong,Gao Wensheng.AR model parameters method for PD pattern recognition based on pulse waveforms[J].High Voltage Engineering,2001,27(3):1-3(in Chinese).
[29]  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.
[30]  Zhang Xiaohong,Zhang Liang,Le Bo,et al.Analysis on aging condition of stator winding insulation of generator based on the moment characteristics of partial discharge[J].Proceedings of the CSEE,2002,22(5):94-98(in Chinese).
[31]  杜伯学,魏国忠.基于小波与分形理论的电力设备局部放电类型识别[J].电网技术,2006,30(13):76-80.
[32]  Du Boxue,Wei Guozhong.Partial discharge classification based on wavelet and fractal theory[J].Power System Technology,2006,30(13):76-80(in Chinese).

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