基于谱峭度分析和粒子群Kmeans算法的高压断路器故障诊断研究
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摘要
为满足电网对高压断路器高效诊断要求,提出一种谱峭度分析和粒子群K均值算法(PSO-Kmeans)相结合的故障诊断方法。该方法首先对正常状态和故障状态振动信号进行快速Kurtogram谱峭度分析,得到谱峭度指标最大中心频率和相应频率分辨率,据此设计带通滤波器对信号进行去噪;对去噪后的信号进行小波分解,提取小波包能量熵作为特征量;进一步采用PSO-Kmeans对特征量进行聚类分析。实验结果表明:改进谱峭度分析法弥补了传统带通滤波器参数确定的不足,提升去噪效果;去噪与PSO-Kmeans算法相结合的诊断方法克服了传统Kmeans易受初始聚类中心影响的缺点,聚类效果良好且精度高于传统算法,证实该方法适用于高精度高压断路器机械故障诊断。
In order to meet the power grid requirement of high precision circuit breaker fault diagnosis,an improved spectral kurtosis analysis combined with PSO Kmeans algorithm is proposed. Firstly,the Kurtogram spectrum analysis is performed for normal and fault vibration signals,the central and the corresponding frequencies are obtained,the band pass filter is designed;The de-noising signal is decomposed by wavelet,the wavelet packet energy entropy is extracted;PSO-Kmeans is used to cluster the feature quantity. It is proved that,the effectiveness of designed filter is verified;The employed PSO-Kmeans algorithm overcomes the shortcoming of traditional Kmeans,achieves good clustering results and improves the accuracy of fault diagnosis. The method is suitable for high-precision mechanical fault diagnosis of high voltage circuit breaker.
In order to meet the power grid requirement of high precision circuit breaker fault diagnosis,an improved spectral kurtosis analysis combined with PSO Kmeans algorithm is proposed. Firstly,the Kurtogram spectrum analysis is performed for normal and fault vibration signals,the central and the corresponding frequencies are obtained,the band pass filter is designed;The de-noising signal is decomposed by wavelet,the wavelet packet energy entropy is extracted;PSO-Kmeans is used to cluster the feature quantity. It is proved that,the effectiveness of designed filter is verified;The employed PSO-Kmeans algorithm overcomes the shortcoming of traditional Kmeans,achieves good clustering results and improves the accuracy of fault diagnosis. The method is suitable for high-precision mechanical fault diagnosis of high voltage circuit breaker.
引文
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[18]钱国超,赵仲勇,邹德旭,等.基于连续小波变换的变压器绕组变形故障类型检测[J].高电压技术,2017,43(6):2016-2023.QIAN Guochao,ZHAO Zhongyong,ZOU Dexu,et al.Detection of transformer winding deformation fault types based on continuous wavelet transform[J].High Voltage Engineering,2017,43(6):2016-2023.
[19]张良县,帅远明,陈荣,等.基于粒子群优化算法的特高压变压器绕组端部角环优化布置研究[J].高压电器,2018,54(9):97-102.ZHANG Liangxian,SHUAI Yuanming,CHEN Rong,et al.Arrangement optimization of angle rings of UHV transformer winding based on PSO algorithm[J].High Voltage Apparatus,2018,54(9):97-102.
[20]曹生让,丁晓群,王庆燕,等.基于反向云自适应粒子群算法的多目标无功优化[J].中国电力,2018,51(7):21-27.CAO Shengrang,DING Xiaoqun,WANG Qingyan,et al.Multi-objective reactive power optimization based on opposition-based learning cloud model adaptive particle swarm optimization[J].Electric Power,2018,51(7):21-27.
[2]赵莉华,付荣荣,荣强,等.基于自适应神经模糊推理系统的高压断路器操作机构状态评估[J].高电压技术,2017,43(6):2007-2015.ZHAO Lihua,FU Rongrong,RONG Qiang,et al.State evaluation of high voltage circuit breaker operating mechanism based on adaptive neural fuzzy inference system[J].High Voltage Engineering,2017,43(6):2007-2015.
[3]孙抗,刘永超.基于EEMD和马氏距离的高压断路器机械故障诊断研究[J].高压电器,2018,54(12):43-49.SUN Kang,LIU Yongchao.Research on mechanical fault diagnosis of high voltage circuit breaker based on EEMDand mahalanobis distance discriminant[J].High Voltage Apparatus,2018,54(12):43-49.
[4]杨元威,关永刚,陈士刚,等.基于声音信号的高压断路器机械故障诊断方法[J].中国电机工程学报,2018,38(22):6730-6736.YANG Yuanwei,GUAN Yonggang,CHEN Shigang,et al.Mechanical fault diagnosis method of high voltage circuit breaker based on sound signal[J].Proceedings of the CSEE,2018,38(22):6730-6736.
[5]程序,关永刚,张文鹏,等.基于因子分析和支持向量机算法的高压断路器机械故障诊断方法[J].电工技术学报,2014,29(7):209-215.CHENG Xu,GUAN Yonggang,ZHANG Wenpeng,et al.Diagnosis method on the mechanical failure of high voltage circuit breakers based on factor analysis and SVM[J].Transactions of China Electrotechnical Society,2014,29(7):209-215.
[6]巫佩军,马少康,陈乔,等.基于特定振动反馈的柔性系统振动抑制方法[J].电工技术学报,2017,32(2):188-194.WU Peijun,MA Shaokang,CHEN Qiao,et al.Resonant suppression of flexible system by feedback compensation of resonant frequencies[J].Transactions of China Electrotechnical Society,2017,32(2):188-194.
[7]KAZEMI R.Data mining of online diagnosed waveforms for probabilistic condition assessment of SF6 circuit breakers[J].IEEE Transactions on Power Delivery,2015,30(3):1354-1362.
[8]王宏超,陈进,董广明,等.基于快速Kurtogram算法的共振解调方法在滚动轴承故障特征提取中的应用[J].振动与冲击,2013,32(1):35-37.WANG Hongchao,CHEN Jin,DONG Guangming,et al.Application of resonance demodulation in rolling bearing fault feature extraction based on fast computation of Kurtogram[J].Journal of Vibration and Shock,2013,32(1):35-37.
[9]LIU A M,LIN X,LIU X D.Fault diagnosis method of high voltage circuit breaker based on(RBF)artificial neural network[C]//Modular Machine Tool&Automatic Manufacturi.[S.l.]:IEEE,2005:1-4.
[10]赵书涛,张佩,申路,等.高压断路器振声联合故障诊断方法[J].电工技术学报,2014,29(7):216-221.ZHAO Shutao,ZHANG Pei,SHEN Lu,et al.Vibration and acoustic joint mechanical fault diagnosis method of high voltage circuit breakers[J].Transactions of China Electrotechnical Society,2014,29(7):216-221.
[11]徐建源,张彬,林莘,等.能谱熵向量法及粒子群优化的RBF神经网络在高压断路器机械故障诊断中的应用[J].高电压技术,2012,38(6):1299-1306.XU Jianyuan,ZHANG Bin,LIN Xin,et al.Application of energy spectrum entropy vector method and RBF neural networks optimized by the particle swarm in high-voltage circuit breaker mechanical fault diagnosis[J].High Voltage Engineering,2012,38(6):1299-1306.
[12]赵科,杨景刚,马速良,等.基于神经网络集成模型在高压断路器机械故障诊断中的应用[J].高压电器,2018,54(7):217-223.ZHAO Ke,YANG Jinggang,MA Suliang,et al.Application of neural network ensemble model in mechanical mault identification of high voltage circuit breaker[J].High Voltage Apparatus,2018,54(7):217-223.
[13]王振浩,顾欣然,孙福军.基于EMD近似熵的高压断路器故障诊断[J].高压电器,2018,54(10):151-156.WANG Zhenhao,GU Xinran,SUN Fujun.High voltage circuit breaker fault diagnosis based on EMD approximate entropy[J].High Voltage Apparatus,2018,54(10):151-156.
[14]黄南天,方立华,王玉强,等.基于局域均值分解和支持向量数据描述的高压断路器机械状态监测[J].电工电能新技术,2017(1):73-80.HUANG Nantian,FANG Lihua,WANG Yuqiang,et al.Machinery condition monitoring of high voltage circuit breakers based on local mean decomposition and support vector data description[J].Advanced Technology of Electrical Engineering and Energy,2017(1):73-80.
[15]刘艳,陈丽安.基于SOM的真空断路器机械故障诊断[J].电工技术学报,2017,32(5):49-54.LIU Yan,CHEN Li’an.Mechanical fault diagnosis of vacuum circuit breaker based on SOM[J].Transactions of China Electrotechnical Society,2017,32(5):49-54.
[16]李春锋,孔海洋,王璇,等.基于PCA和聚类的断路器分合闸线圈电流研究[J].电力与能源,2016(1):32-36.LI Chunfeng,KONG Haiyang,WANG Xuan,et al.Research of the circuit breakers switching coil currents based on PCA and clustering algorithm[J].Power&Energy,2016(1):32-36.
[17]谢秀华,李陶深.一种基于改进PSO的K-means优化聚类算法[J].计算机技术与发展,2014,24(2):34-38.XIE Xiuhua,LI Taoshen.An optimized K-means clustering algorithm based on improved particle swarm optimization[J].Computer Technology and Development,2014,24(2):34-38.
[18]钱国超,赵仲勇,邹德旭,等.基于连续小波变换的变压器绕组变形故障类型检测[J].高电压技术,2017,43(6):2016-2023.QIAN Guochao,ZHAO Zhongyong,ZOU Dexu,et al.Detection of transformer winding deformation fault types based on continuous wavelet transform[J].High Voltage Engineering,2017,43(6):2016-2023.
[19]张良县,帅远明,陈荣,等.基于粒子群优化算法的特高压变压器绕组端部角环优化布置研究[J].高压电器,2018,54(9):97-102.ZHANG Liangxian,SHUAI Yuanming,CHEN Rong,et al.Arrangement optimization of angle rings of UHV transformer winding based on PSO algorithm[J].High Voltage Apparatus,2018,54(9):97-102.
[20]曹生让,丁晓群,王庆燕,等.基于反向云自适应粒子群算法的多目标无功优化[J].中国电力,2018,51(7):21-27.CAO Shengrang,DING Xiaoqun,WANG Qingyan,et al.Multi-objective reactive power optimization based on opposition-based learning cloud model adaptive particle swarm optimization[J].Electric Power,2018,51(7):21-27.