全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电网技术  2010 

粒子群优化-最小二乘支持向量机算法在高压断路器机械故障诊断中的应用

, PP. 197-200

Full-Text   Cite this paper   Add to My Lib

Abstract:

高压断路器机械故障诊断中难以获得大量故障数据样本,从而在一定程度上制约了基于知识的方法在实际故障诊断中的应用。针对这一问题,利用支持向量机(SVM)在小样本情况下具有较强分类能力的特点,提出了一种基于最小二乘支持向量机(LSSVM)的高压断路器机械故障诊断方法。最小二乘支持向量机中参数选择非常重要,它决定着故障诊断的精确度,为了提高断路器故障诊断的精度和效率,本文利用粒子群算法(PSO)来对最小二乘支持向量机的进行参数优化。试验结果证明PSO-LSSVM不仅能够取得良好的分类效果,而且诊断速度与精度高于传统SVM,更适合在断路器机械故障诊断中应用。

References

[1]  Runde M,Skyberg B,Ohlen M.Vibration analysis for periodic diagnostic testing of circuit breakers[C].High Voltage Engineering Symposium,Conference Publication,London,UK,1999:98-101.
[2]  王振浩,杜凌艳,李国庆,等.动态时间规整算法诊断高压断路器故障[J].高电压技术,2006,32(10):36-38.
[3]  Wang Zhenhao,Du Lingyan,Li Guoqing,et al.Fault diagnosis of high voltage circuit breakers based on dynamic time warping algorithm[J].High Voltage Engineering,2006,32(10):36-38(in Chinese).
[4]  Lee D S S,Lithgow B J,Morrison R E.New fault diagnosis of circuit breakers[J].IEEE Transactions on Power Delivery,2003,18(2):454-459.
[5]  赵海龙,王芳,胡晓光.小波包-能量谱在高压断路器机械故障诊断中的应用[J].电网技术,2004,28(6):46-48.
[6]  Zhao Hailong,Wang Fang,Hu Xiaoguang.Application of wavelet packet-energy Spectrum in mechanical fault diagnosis of high voltage circuit breakers[J].Power System Technology,2004,28(6):46-48(in Chinese).
[7]  孙来军,胡晓光,纪延超,等.小波包-特征熵在高压断路器故障诊断中的应用[J].电力系统自动化,2006,30(14):62-65.
[8]  陈伟根,范海炉,王有元,等.基于小波能量与神经网络的断路器振动信号识别方法[J].电力自动化设备,2008,28(2):29-32.
[9]  Chen Weigen,Fan Hailu,Wang Youyuan,et al.Circuit breaker vibration signal recognition based on wavelet energy and neural network[J].Electric Power Automation Equipment,2008,28(2):29-32(in Chinese).
[10]  孙来军,胡晓光,纪延超.一种基于振动信号的高压断路器故障诊断新方法[J].中国电机工程学报,2006,26(6):157-161.
[11]  Sun Laijun,Hu Xiaoguang,Ji Yanchao.A new method of fault diagnosis for high voltage circuit breakers based on vibration signals [J].Proceedings of the CSEE,2006,26(6):157-161(in Chinese).
[12]  孙来军,胡晓光,纪延超.基于支持向量机的高压断路器机械状态分类[J].电工技术学报,2006,21(8):53-58.
[13]  Runde H K.Continuous monitoring of circuit breakers using vibration analysis[J].IEEE Transactions on Power Delivery,2005,20(4):2458-2465.
[14]  Sun Laijun,Hu Xiaoguang,Ji Yanchao,et al.Fault diagnosis for HV circuit breakers with characteristic entropy of wavelet packet[J].Automation of Electric Power Systems,2006,30(14):62-65(in Chinese).
[15]  Sun Laijun,Hu Xiaoguang,Ji Yanchao.Mechanical fault classification of high voltage circuit breakers based on support vector machine[J].Transactions of China Electrotechnical Society,2006,21(8):53-58(in Chinese).
[16]  Burges C J C.A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery,1998,2(2):121-167.
[17]  赵文清,朱永利,张小奇.应用支持向量机的变压器故障组合预测[J].中国电机工程学报,2008,28(25):14-18.
[18]  Zhao Wenqing,Zhu Yongli,Zhang Xiaoqi.Combinational forecast for transformer faults based on support vector machine[J].Proceeding of the CSEE,2008,28(25):14-18(in Chinese).
[19]  Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[20]  Eberhart R C,Kennedy J.A new optimizer using particle swarm theory[C].Proceedings Sixth Symposium on Micro Machine and Human Science,Nagoya,1995:39-43.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133