基于改进掩膜信号优化的经验模态分解算法的有载分接开关机械故障诊断
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摘要
为了有效在线监测有载分接开关(OLTC)的运行状况,采用基于改进掩膜信号优化的经验模态分解算法和区间最大功率特征矩阵相结合方法对有载分接开关运行过程中产生的振动信号进行分析。通过在采集到的原始信号中加入改进的掩膜信号,有效地消除经验模态分解(EMD)过程中出现的模态混叠现象。然后根据分解得到的固有模态函数(IMF)求得区间最大功率特征矩阵,从而进一步对有载分接开关的实际运行状态进行监测。计算结果显示OLTC在不同运行状态下的区间最大功率特征矩阵有明显的差异,其区间最大功率特征矩阵相似度指标可较好地判别OLTC发生典型故障时振动信号的差异程度。
In order to effectively monitor the operation of the on-load tape-changer(OLTC)online,an empirical mode decomposition algorithm based on improved mask signal optimization and a characteristic matrix phase method of energy spectrum are proposed to analyze the vibration signals on the surface of the switched on load changers.An improved mask signal is added to the collected original signal,which can effectively eliminate the modal aliasing in the process of empirical mode decomposition(EMD).Then the maximum power characteristic matrix is obtained according to the decomposed intrinsic mode function(IMF),monitoring condition of the onload tap switch.The results show that there are significant differences in the interval maximum power characteristic matrix under different working conditions,and the degree of recognition index of the interval maximum power characteristic matrix can be used to distinguish the difference degree of vibration signals in OLTC typical faults.
In order to effectively monitor the operation of the on-load tape-changer(OLTC)online,an empirical mode decomposition algorithm based on improved mask signal optimization and a characteristic matrix phase method of energy spectrum are proposed to analyze the vibration signals on the surface of the switched on load changers.An improved mask signal is added to the collected original signal,which can effectively eliminate the modal aliasing in the process of empirical mode decomposition(EMD).Then the maximum power characteristic matrix is obtained according to the decomposed intrinsic mode function(IMF),monitoring condition of the onload tap switch.The results show that there are significant differences in the interval maximum power characteristic matrix under different working conditions,and the degree of recognition index of the interval maximum power characteristic matrix can be used to distinguish the difference degree of vibration signals in OLTC typical faults.
引文
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[2]石鑫,朱永利,王刘旺,等.基于深度信念网络的电力变压器故障分类建模[J].电力系统保护与控制,2016,44(1):71-76.SHI Xin,ZHU Yongli,WANG Liuwang,et al.Powertransformer fault classifying model based on deep beliefnetwork[J].Power System Protec-tion and Control,2016,44(1):71-76.
[3]LI Z,YE L,ZHAO Y,etal.Short-term wind powerprediction based on extreme learning machine with errorcorrection[J].Protection and Control of Modern PowerSystems,2016,1(1):9-16.
[4]张琳,马宏忠,王涛云,等.基于振动-SVM的变压器绕组缺陷诊断方法[J].陕西电力,2016,44(1):14-18.ZHANG Lin,MA Hongzhong,WANG Taoyun.et al.Feasibility study using vibration measurement to on-line detect contact condition of OLTC[J].Shaanxi Electric Power,2016,44(1):14-1 8.
[5]LI G N,HU Y P,CHEN HX,et al.Fault diagnosis method of transformer winding based on vibration-SVM[J].Energy and Buildings,2016,1 16:104-1 13.
[6]崔杨柳,马宏忠,许洪,华,等.基于振动的变压器绕组松动诊断方法[J].广东电力,2017,30(9):118-121.CUI YangLiu,MA Hongzhong,XU Honghua,et al.Vibration-based diagnosis method for transformer winding loosening[J].GuangDong Electric Power,2017,30(9):118-121.
[7]李中,宋天慧,郭通,等.不同负载电流下的变压器表面三维振动信号特征分析[J].电力系统保护与控制,2017,45(12):29-34.LI Zhong,SONG Tianhui,GUO Tong,et al.Analysis of three-dimensional vibration signal characteristics of transformer surface under different load current[J].Power System Protection and Control,2017,45(12):29-34.
[8]邓小明,邓小文,王丰华,等.电力变压器绕组轴向模态特性试验研究[J].广东电力,2017,30(7):115-120.DENG Xiaoming,DENG Xiaowen,WANG Feihua,et al.Experimental study on axial modal characteristics of power transformer winding[J].Guangdong Electric Power.2017,30(7):115-120.
[9]杨毅,张楚,刘石,等.基于振动的油浸式电力变压器短路累积效应试验研究[J].广东电力,2016,29(12):115-120.YANG Yi,ZHAHNG Chu,LIU Shi,et al.Experimental study on short-circuit accumulation effect of oil-immersed power transformer based on vibration[J].Guangdong Electric Power.2016,29(12):115-120.
[10]刘力,毕贵红,祖哲,等.基于掩膜分量的改进HHT方法在电能质量扰动信号定位中的应用[J].电气自动化,2013,35(5):55-57.LIU Li,BI Guihong,ZU Zhe,et al.Application of improved HHT method based on mask component in the location of power quality disturbance signal[J].Electric Automation,2013,35(5):55-57.
[11]徐广玉,邱继辉,沈少萍.基于掩膜经验模态分解和ELM的风速预测[J].兵工自动化,2017,36(5):25-29.XU Guangyu,QIU Jihui,SHEN Shaoping.Based on mask empirical mode decomposition and ELM wind speed prediction[J].Ordnance Industry Automation,2017,36(5):25-29.
[12]张娜,王守相,王亚昊.基于掩膜经验模态分解的风速组合预测模型[J].中国电力,2014,47(5):129-135.ZHANG Na,WANG Shouxiang,WANG Yahao.Prediction model of wind speed combination based on mask empirical mode decomposition[J].Electric Power,2014,47(5):129-135.
[13]王恩俊,张建文,马晓伟,等.基于CEEMD-EEMD的局部放电阈值去噪新方法[J].电力系统保护与控制,2016,44(15):93-98.WANG Enjun,ZHANG Jianwen,MA Xiaowei,et al.A new threshold denoising algorithm for partial dischargebased on CEEMD-EEMD[J].Power System Protection and Control,2016,44(15):93-98.
[14]CANDANEDO L M,FELDHEIM V,DERAMAIX D.Data driven prediction models of energy use of appliances ina low-energy house[J].Energy&Buildings,2017,140:81-97.