基于频响曲线稀疏表示的变压器绕组变形模式识别方法

刘云鹏; 程槐号; 胡焕; 张重远

刘云鹏,程槐号,胡焕,张重远.基于频响曲线稀疏表示的变压器绕组变形模式识别方法[J].电测与仪表,2018,55(5):14-21.
LIU Yunpeng,CHENG Huaihao,HU Huan,ZHANG Zhongyuan.A Transformer Winding Deformation Pattern Recognition Method Based on Sparse Representation of Frequency Response Curve[J].Electrical Measurement & Instrumentation,2018,55(5):14-21.

基于频响曲线稀疏表示的变压器绕组变形模式识别方法

A Transformer Winding Deformation Pattern Recognition Method Based on Sparse Representation of Frequency Response Curve

DOI：

中文关键词: 绕组变形稀疏表示匹配追踪支持向量机

英文关键词:winding deformation sparse representation matching pursuit support vector machine (SVM)

基金项目:广东电网公司科技项目(GDKJ00000021)。ScienceSandSTechnologyproject of Guangdong power grid corporation(GDKJ00000021).

作者中文名	作者英文名	单位
刘云鹏	LIU Yunpeng	河北省输变电设备安全防御重点实验室华北电力大学
程槐号	CHENG Huaihao	河北省输变电设备安全防御重点实验室华北电力大学
胡焕	HU Huan	河北省输变电设备安全防御重点实验室华北电力大学
张重远	ZHANG Zhongyuan	河北省输变电设备安全防御重点实验室华北电力大学

摘要点击次数: 1822

中文摘要:

为了提高电力变压器绕组状态监测水平,提出了一种基于频率响应曲线稀疏表示的绕组变形模式识别方法。该模式识别方法的核心是信号稀疏表示理论中的匹配追踪算法。在构建了Gabor原子的过完备原子库和通过有限元模型仿真得到了正常及变形绕组频响曲线的基础上,将正常情况及变形情况下的绕组频响曲线在过完备原子库上进行稀疏表示,并对所有匹配的Gabor原子分别进行短频傅里叶变换、叠加,得到正常曲线及变形曲线的等效时频分布,再将两条曲线的等效时频分布值相减,得到可以反映变压器频响曲线变形程度的特征向量。最后,利用支持向量机模型实现了不同绕组变形故障的识别。试验结果表明,本文提出的方法具有较高的可靠性,适用于变压器绕组变形模式识别。

英文摘要:

In order to improve the level of power transformer winding condition monitoring, a winding deformation pattern recognition method based on sparse decomposition of frequency response curve was proposed. The core of this method is the matching pursuit algorithm in the signal sparse representation theory. Moreover, the over-complete dictionary of Gabor atoms was constructed and the simulation curves of frequency response were obtained by the finite element model of transformer. Based upon these, the sparse representation of the frequency response curves of winding under normal condition and deformation condition was carried out on the over-complete dictionary. Then, the short frequency Fourier transform and superposition of all corresponding matched Gabor atoms were carried out to obtain the equivalent time-frequency distribution of the normal curve and deformation curve. Finally the time-frequency distribution values of two different curves were subtracted in order to obtain the feature vector of pattern recognition, which also could be used as a criterion to reflect the degree of deformation of transformer frequency response curve. Finally, support vector machine (SVM) is used to realize the identification of the simulation curve of different winding deformation. The simulation results show that the method proposed in this paper has a high reliability, and is suitable for transformer winding deformation pattern recognition.

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