小波包变换与支持向量机的电力变压器故障诊断方法
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1.国网安徽省电力有限公司设备部;2.上海工程技术大学电子电气工程学院

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Fault diagnosis method of power transformer based on wavelet packet transform and support vector machine
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1.Equipment Department,State Grid Anhui Electric Power Co,Ltd,Hefei Anhui;2.College of Electronic and Electrical Engineering

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    摘要:

    传统电力变压器故障诊断方法无法实现电力故障准确检测,并保证电力变压器正常运行,为此,研究小波包变换与支持向量机的电力变压器故障诊断方法。对采集的电力变压器电力信号,利用改进最小噪声分离变换去噪(MNF)实施去噪,并通过加权邻域均值法对噪声矩阵进行估计,完成估计后利用改进的MNF变换有效实现图像降维以及去噪处理,提取信号特征,利用小波包变换方法将信号分为信号低频部分与高频部分,以获取小波包能量特征向量,将所获取小波包能量特征向量输入支持向量机分类器中,利用支持向量机分类器输出结果实现电力变压器状态识别和故障诊断。实验结果表明,采用该方法可有效诊断电力变压器中的铁芯短路、线圈层间短路、套管对地击穿、线圈绝缘电阻下降和套管间放电等故障,故障诊断精度高于98.5%。

    Abstract:

    The traditional fault diagnosis methods of power transformer can not detect the power fault accurately and ensure the normal operation of power transformer. Therefore, the fault diagnosis method of power transformer based on wavelet packet transform and support vector machine is studied. For the collected power transformer power signal, the improved minimum noise separation transform denoising (MNF) is used to denoise, and the noise matrix is estimated by the weighted neighborhood mean method. After the estimation, the improved MNF transform is used to effectively realize image dimensionality reduction and denoising, extract the signal characteristics, and divide the signal into low-frequency part and high-frequency part by wavelet packet transform, In order to obtain the wavelet packet energy feature vector, the obtained wavelet packet energy feature vector is input into the support vector machine classifier, and the output results of the support vector machine classifier are used to realize the state recognition and fault diagnosis of power transformer. The experimental results show that this method can effectively diagnose the faults in power transformer, such as iron core short circuit, coil interlayer short circuit, bushing to ground breakdown, coil insulation resistance drop and bushing to bushing discharge, and the fault diagnosis accuracy is higher than 98.5%.

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  • 收稿日期:2022-02-24
  • 最后修改日期:2022-04-15
  • 录用日期:2022-04-22
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