逆水寒帮会倒买倒卖攻略|逆水寒官网电脑配置|

[1]王庆燕,曹生让,陈秉岩,等.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[J].高压电器,2019,55(05):23-28,34.[doi:DOI:10.13296/j.1001-1609.hva.2019.05.004]
 WANG Qingyan,CAO Shengrang,CHEN Bingyan,et al.Study on Fault Diagnosis of High Voltage Circirt Breaker Based on Spectral Kurtosis Analysis and Particle Swarm Optimization Kmeans Clustering Algorithm[J].High Voltage Apparatus,2019,55(05):23-28,34.[doi:DOI:10.13296/j.1001-1609.hva.2019.05.004]
点击复制

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()
分享到:

《高压电器》[ISSN:1001-1609/CN:61-11271/TM]

卷:
第55卷
期数:
2019年05期
?#38472;?
23-28,34
栏目:
2018 高电压与放电等离子体专题
出版日期:
2019-05-20

文章信息/Info

Title:
Study on Fault Diagnosis of High Voltage Circirt Breaker Based on Spectral Kurtosis Analysis and Particle Swarm Optimization Kmeans Clustering Algorithm
作者:
王庆燕12 曹生让3 陈秉岩3 杨忠2
(1. 金陵科技学院机电学院,南京211169;2. 南京航空航天大学自动化学院, 南京211106;3. 河海大学能源与电气学院,南京210098)
Author(s):
WANG Qingyan12CAO Shengrang3CHEN Bingyan3YANG Zhong2
(1. Jinling Institute of Technology Electrical Engineering,Nanjing 211169,China;2. College of Automation Engineering Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;3. College of Energy and Electrical Engineering, Hohai University,Nanjing 210098,China)
关键词:
机械故障诊断高压断路器谱峭度Kurtogram算法粒子群算法
Keywords:
mechanical fault identificationhigh voltage circuit breakerspectral kurtosisKurtogram algorithm particle swarm optimization algorithm
DOI:
DOI:10.13296/j.1001-1609.hva.2019.05.004
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?#34892;?#39057;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?#24555;?#20316;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?#34892;?#24433;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。
Abstract:
In order to meet the power grid requirement of high precision circuit breaker fault diagnosis,an im? proved spectral kurtosis analysis combined with PSO Kmeans algorithm is proposed. Firstly,the Kurtogram spec? trum 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 en? ergy 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?preci? sion mechanical fault diagnosis of high voltage circuit breaker.

参考文献/References:

[1] 关?#26639;眨?#26472;元威,钟建英,等. 高压断路器机械故障诊断 方法综述[J].高压电器,2018,54(7):10?19. GUAN Yonggang,YANG Yuanwei,ZHONG Jianying,et al. Review on mechanical fault diagnosis methods for high?volt? age circuit breakers[J]. High Voltage Apparatus,2018,54 (7):10?19.
[2] 赵莉华,?#24230;?#33635;,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 eval? uation 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 EEMD and mahalanobis distance discriminant[J]. High VoltageApparatus,2018,54(12):43?49.
[4] 杨元威,关?#26639;眨?#38472;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?#26639;眨?#24352;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 Electrotech? nical 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. Ap? plication 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 net? work[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 Electro? technical 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 Volt? age 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?#38047;?#22343;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. Ma? chinery condition monitoring of high voltage circuit break? ers based on local mean decomposition and support vector data description[J]. Advanced Technology of Electrical En? gineering 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 vacu? um 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. Re? search 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?#35889;?#21464;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. Detec? tion of transformer winding deformation fault types based on continuous wavelet transform[J]. High Voltage Engineer? ing,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?#35889;?#31471;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 transform? er winding based on PSO algorithm[J]. High Voltage Appa? ratus,2018,54(9):97?102.
[20] 曹生让,丁晓群,王庆燕,等. 基于反向?#35889;?#36866;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 oppo? sition?based learning cloud model adaptive particle swarm optimization[J]. Electric Power,2018,51(7):21?27.

备注/Memo

备注/Memo:
收稿日期:2018?11?01; 修回日期:2019?01?06 基金项目?#33322;?#33487;省高校自然科学基金(17KJB470005);博士科研启动基金(jit-B-201626)。 Project Supported by Natural Science Fund of Colleges and Universities in Jiangsu Province(17KJB470005),the Doctoral Scientific Research Foundation(jit-B-201626).王庆燕(1981—),女,博士,讲师,研究方向为电力设备故 障诊断(通讯作者)。 杨忠(1969—),?#26657;?#25945;授,博士生导师,研究方向为智能 测控与诊断。
更新日期/Last Update: 2019-05-20
逆水寒帮会倒买倒卖攻略