用ILMD多尺度时频熵识别直流牵引网振荡电流与故障电流
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摘要
针对城市轨道交通直流牵引网的振荡电流容易引起继电保护系统频繁误动的问题,提出了一种基于改进局部均值分解的多尺度时频熵识别直流牵引网振荡电流与短路故障电流方法。利用改进局部均值分解法分析直流牵引网的馈线电流信号,获得其时频分布;将信息熵理论引入时频分布,对时频平面进行频段划分,计算各频段的时频熵,求出时频平面的整体多尺度时频熵;定量描述馈线电流信号的能量在时频平面分布的均匀性,均匀性的不同可以反应直流牵引网运行状态的差别,从而可通过多尺度时频熵的大小区分直流牵引网的振荡电流与短路故障电流。算例分析验证了该方法的有效性。
Aiming at frequent malfunctions of the relay protection system caused by the oscillation current in DC traction network of urban rail transit, we propose a method for identifying the oscillation current and short-circuit fault current in DC traction network based on improved local mean decomposition(ILMD) and multi-scale time-frequency entropy. Firstly, the ILMD method is used to analyze the feeder current signal in DC traction network so that the time-frequency distribution of the feeder current signal can be obtained. Secondly, the Shannon entropy theory is introduced into the time-frequency distribution, and the time-frequency plane is divided into some frequency channels. Then the time-frequency entropy of each frequency channel is figured out. Finally, the overall multi-scale time-frequency entropy of the time-frequency plane is acquired, which can be used to quantitatively express the uniformity of the distribution of the feeder current signal's energy in the plane. Differences in uniformity can indicate differences among operating states of DC traction network, which makes it possible to distinguish the oscillation current and short-circuit fault current in DC traction network by means of multi-scale time-frequency entropy. On the basis of simulation analysis and field data calculation, availability of the suggested approach is certified.
Aiming at frequent malfunctions of the relay protection system caused by the oscillation current in DC traction network of urban rail transit, we propose a method for identifying the oscillation current and short-circuit fault current in DC traction network based on improved local mean decomposition(ILMD) and multi-scale time-frequency entropy. Firstly, the ILMD method is used to analyze the feeder current signal in DC traction network so that the time-frequency distribution of the feeder current signal can be obtained. Secondly, the Shannon entropy theory is introduced into the time-frequency distribution, and the time-frequency plane is divided into some frequency channels. Then the time-frequency entropy of each frequency channel is figured out. Finally, the overall multi-scale time-frequency entropy of the time-frequency plane is acquired, which can be used to quantitatively express the uniformity of the distribution of the feeder current signal's energy in the plane. Differences in uniformity can indicate differences among operating states of DC traction network, which makes it possible to distinguish the oscillation current and short-circuit fault current in DC traction network by means of multi-scale time-frequency entropy. On the basis of simulation analysis and field data calculation, availability of the suggested approach is certified.
引文
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[14]武哲,杨绍普,张建超.基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用[J].振动与冲击,2016,35(3):7-13.WU Zhe,YANG Shaopu,ZHANG Jianchao.Bearing fault feature extraction method based on LMD adaptive multiscale morphology and energy operator demodulating[J].Journal of Vibration and Shock,2016,35(3):7-13.
[15]WANG Y,HE Z,ZI Y.A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis[J].Journal of Vibration and Acoustics,2010,132(2):0210101.
[16]胡劲松,杨世锡,任达千.基于样条的振动信号局域均值分解方法[J].数据采集与处理,2009,24(1):82-86.HU Jinsong,YANG Shixi,REN Daqian.Spline-based local mean decomposition method for vibration signal[J].Journal of Data Acquisition and Processing,2009,24(1):82-86.
[17]邓林峰,赵荣珍,靳伍银.基于改进LMD方法的故障转子振动分析[J].振动、测试与诊断,2015,35(4):702-708.DENG Linfeng,ZHAO Rongzhen,JIN Wuyin.Vibration analysis of fault rotor based on the improved local mean decomposition[J].Journal of Vibration,Measurement&Diagnosis,2015,35(4):702-708.
[18]李慧梅,安钢,黄梦.基于局部均值分解的边际谱在滚动轴承故障诊断中的应用[J].振动与冲击,2014,33(3):5-8.LI Huimei,AN Gang,HUANG Meng.Application of marginal spectrum based on local mean decomposition in rolling bearing fault diagnosis[J].Journal of Vibration and Shock,2014,33(3):5-8.
[19]YU D,YANG Y,CHENG J.Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis[J].Measurement,2007,40(9/10):823-830.
[20]LUO G,ZHANG D,KOH Y K,et al.Time-frequency entropy-based partial-discharge extraction for nonintrusive measurement[J].IEEETransactions on Power Delivery,2012,27(4):1919-1927.
[2]祝冰心.直流牵引网故障电流识别算法研究[D].北京:北京化工大学,2014.ZHU Bingxin.DC traction current network fault identification algorithm[D].Beijing,China:Beijing University of Chemical Technology,2014.
[3]LI Y H,TIAN X J,LI X Q.Nonlinear second-order model and equilibrium point characteristic analysis of DC traction power supply system[J].ISRN Applied Mathematics,2013:221-228.
[4]李夏青,左丽.直流牵引网故障电流能量谱及特征矢量[J].电工技术学报,2010,25(11):164-168.LI Xiaqing,ZUO Li.Energy spectrum and eigenvector of DC traction power supply short-circuit current[J].Transactions of China Electrotechnical Society,2010,25(11):164-168.
[5]倪涛,李夏青.基于EMD分解的直流牵引供电系统短路故障识别[J].北京石油化工学院学报,2012,20(2):13-17.NI Tao,LI Xiaqing.The identification to short-circuit in DC traction power supply based on EMD decomposition[J].Journal of Beijing Institute of Petro-Chemical Technology,2012,20(2):13-17.
[6]田行军,李夏青,李运华.直流牵引网振荡电流与故障电流波形识别[J].电工技术学报,2013,28(11):247-253.TIAN Xingjun,LI Xiaqing,LI Yunhua.Waveform identification of oscillation current and fault current in DC traction network[J].Transactions of China Electrotechnical Society,2013,28(11):247-253.
[7]祝冰心,李夏青,田行军,等.基于关联维数的直流牵引网故障识别[J].北京石油化工学院学报,2014,22(1):37-43.ZHU Bingxin,LI Xiaqing,TIAN Xingjun,et al.Fault identificaiton algorithm of DC traction network based on correlation dimension[J].Journal of Beijing Institute of Petro-Chemical Technology,2014,22(1):37-43.
[8]孙鹏,曹雨晨,刘洋,等.采用二进制蚁群模糊神经网络的配电网故障分类方法[J].高电压技术,2016,42(7):2063-2072.SUN Peng,CAO Yuchen,LIU Yang,et al.Fault classification technique for power distribution network using binary ant colony algorithm and fuzzy neural network[J].High Voltage Engineering,2016,42(7):2063-2072.
[9]杨丰源,宋辉,程序,等.基于改进EEMD和Cohen类的局部放电信号联合时频分析[J].高电压技术,2016,42(7):2345-2352.YANG Fengyuan,SONG Hui,CHENG Xu,et al.Analysis of joint time-frequency analysis on partial discharge signal based on improved EEMD and Cohen’s class[J].High Voltage Engineering,2016,42(7):2345-2352.
[10]杨锡运,曹超,任杰,等.利用储能系统平滑光伏波动的模糊聚类经验模态分解方法[J].高电压技术,2016,42(7):2127-2133.YANG Xiyun,CAO Chao,REN Jie,et al.Control method of smoothing PV power output with battery energy storage system based on fuzzy ensemble empirical mode decomposition[J].High Voltage Engineering,2016,42(7):2127-2133.
[11]缪希仁,林瑞聪.基于关联维数与极端学习机的高压输电线路雷击过电压故障识别[J].高电压技术,2016,42(5):1519-1526.MIAO Xiren,LIN Ruicong.Lightning over-voltage fault identification of high-voltage transmission line based on correlation dimension and extreme learning machine[J].High Voltage Engineering,2016,42(5):1519-1526.
[12]SMITH J S.The local mean decomposition and its application to EEGperception data[J].Journal of the Royal Society Interface,2005,2(5):443-454.
[13]杨德昌,唐巍,屈瑞谦,等.基于改进局部均值分解的低频振荡参数提取[J].中国电机工程学报,2013,33(4):135-140.YANG Dechang,TANG Wei,QU Ruiqian,et al.Extraction of the oscillatory parameters of low frequency oscillation based on improved local mean decomposition[J].Proceedings of the CSEE,2013,33(4):135-140.
[14]武哲,杨绍普,张建超.基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用[J].振动与冲击,2016,35(3):7-13.WU Zhe,YANG Shaopu,ZHANG Jianchao.Bearing fault feature extraction method based on LMD adaptive multiscale morphology and energy operator demodulating[J].Journal of Vibration and Shock,2016,35(3):7-13.
[15]WANG Y,HE Z,ZI Y.A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis[J].Journal of Vibration and Acoustics,2010,132(2):0210101.
[16]胡劲松,杨世锡,任达千.基于样条的振动信号局域均值分解方法[J].数据采集与处理,2009,24(1):82-86.HU Jinsong,YANG Shixi,REN Daqian.Spline-based local mean decomposition method for vibration signal[J].Journal of Data Acquisition and Processing,2009,24(1):82-86.
[17]邓林峰,赵荣珍,靳伍银.基于改进LMD方法的故障转子振动分析[J].振动、测试与诊断,2015,35(4):702-708.DENG Linfeng,ZHAO Rongzhen,JIN Wuyin.Vibration analysis of fault rotor based on the improved local mean decomposition[J].Journal of Vibration,Measurement&Diagnosis,2015,35(4):702-708.
[18]李慧梅,安钢,黄梦.基于局部均值分解的边际谱在滚动轴承故障诊断中的应用[J].振动与冲击,2014,33(3):5-8.LI Huimei,AN Gang,HUANG Meng.Application of marginal spectrum based on local mean decomposition in rolling bearing fault diagnosis[J].Journal of Vibration and Shock,2014,33(3):5-8.
[19]YU D,YANG Y,CHENG J.Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis[J].Measurement,2007,40(9/10):823-830.
[20]LUO G,ZHANG D,KOH Y K,et al.Time-frequency entropy-based partial-discharge extraction for nonintrusive measurement[J].IEEETransactions on Power Delivery,2012,27(4):1919-1927.