Wavelet Multi-Resolution Analysis Used for Partial Discharge Pattern Recognition

Conference Record of the 2006 Twenty-Seventh International Power Modulator Symposium Pub Date : 2006-05-14 DOI:10.1109/MODSYM.2006.365193

Ji Yang, Lin Du, You Yuanwang

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引用次数: 1

Abstract

Partial discharge pattern recognition is considered as an effective tool for insulation fault diagnosis on high voltage electric apparatus. Based on the multi-resolution theory of wavelet, a new image recognition method is presented in this paper, which is used in gray intensity image recognition formed by partial discharge phase resolved distribution pattern. Firstly, the outstanding recognition image of partial discharge and the reference one are decomposed by wavelet, and then pattern similarity-degree and adjacency-degree are calculated from the correlation coefficients of low-frequency sub-image at the different scale. The discharge samples extracted from discharge model experiment are processed to pattern recognition by maximum pattern adjacency-degree. According to the recognition rate of multi-resolution image decomposition with four typical wavelet function, the influence of wavelet decomposition scale, wavelet orthogonality and its smoothing continuity to recognizing rate is analyzed. The results show that a satisfied recognition effect has reached through the new method by choosing orthogonal wavelet and suitable decomposition scale

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小波多分辨率分析用于局部放电模式识别

局部放电模式识别被认为是高压电器绝缘故障诊断的有效工具。基于小波的多分辨率理论，提出了一种新的图像识别方法，用于局部放电相位分解分布模式形成的灰度图像识别。首先对局部放电突出识别图像和参考图像进行小波分解，然后根据不同尺度下低频子图像的相关系数计算模式相似度和邻接度;利用最大模式邻接度对放电模型实验中提取的放电样本进行模式识别。根据四种典型小波函数分解多分辨率图像的识别率，分析了小波分解尺度、小波正交性及其平滑连续性对图像识别率的影响。结果表明，通过选择正交小波和合适的分解尺度，新方法取得了满意的识别效果

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Conference Record of the 2006 Twenty-Seventh International Power Modulator Symposium

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