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文章摘要
基于暂态过电压下行波分析的变压器绕组变形在线故障定位方法
On-line Fault Location Method for Transformer Winding Deformation Based on Traveling Wave Analysis with Transient Overvoltage
Received:January 29, 2016  Revised:April 02, 2016
DOI:
中文关键词: 变压器绕组变形  在线故障定位  暂态过电压  行波分析  CEEMDAN
英文关键词: transformer winding deformation, on-line fault location, transient overvoltage, traveling wave analysis, CEEMDAN
基金项目:中央高校基本科研业务费专项资金资助项目(2014xs74)
Author NameAffiliationE-mail
Zhang Ning* State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University zhang_ning26@126.com 
Zhu Yongli State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University yangwang131@126.com 
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中文摘要:
      绕组变形是变压器内部的主要故障类型之一,严重威胁电力系统正常运行。为有效提高绕组变形在线检测的准确性,结合变压器在运行中遭受暂态过电压冲击的特性,提出基于暂态过电压下行波分析的变压器绕组变形在线故障定位方法。当暂态过电压信号侵入变压器绕组时,在绕组末端获取电压行波信号,采用具有自适应白噪声的完整集成经验模态分解方法(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN)对其进行分解,得到本征模态分量(Intrinsic Mode Function, IMF),计算各IMF分量下的相关系数,对比分析后选取一些IMF的相关系数作为绕组变形的故障特征量,最后结合BP神经网络构建故障特征和故障点的映射关系,实现绕组变形的在线故障定位。仿真结果验证了本方法的可行性。
英文摘要:
      Winding deformation is one of transformer faults of the main types, which threatens the normal operation of power system. To effectively increase the accuracy of detecting the winding deformation on-line, combining with the characteristics of transient overvoltage the surge of which the transformer suffers during its operation, an on-line fault location method for transformer winding deformation based on traveling wave analysis with transient overvoltage is proposed. Voltage signal of traveling wave is measured when transient overvoltage invades the transformer winding. Intrinsic mode function(IMF) is obtained through complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) of the signal. Correlation coefficients of the IMF components are calculated, and some correlation coefficients of IMF are chosen as fault feature of winding deformation through the comparison and analysis. Finally, the BP neural network is used to establish the mapping relationship between fault characteristics and fault locations, and the on-line fault location of winding deformation is achieved. The simulation results show the feasibility of the proposed method.
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