基于白化滤波和RBF-KF的变压器局部放电超声波信号提取

李利华; 杨新志; 鲍鹏; 慎志勇; 刘扬

doi:10.13296/j.1001-1609.hva.2026.06.009

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基于白化滤波和RBF-KF的变压器局部放电超声波信号提取

研究与分析 | 更新时间：2026-06-10

- 基于白化滤波和RBF-KF的变压器局部放电超声波信号提取
- Ultrasonic Signal Extraction of Transformer Partial Discharge Based on Whitening Filter and RBF-KF
- 高压电器 2026年62卷第6期页码：76-83
- 作者机构：
  
  中国长江电力股份有限公司，北京 100033
- 作者简介：
  
  李利华（1971—），男，本科，高级工程师，主要研究方向发电厂电气一次设备运行维护等（E-mail：li_lihua@ctg.com.cn）。
  杨新志（1982—），男，硕士，高级工程师，研究方向为发电厂电气一次设备运行维护（通信作者）（E-mail：289393756@qq.com）。
- 基金信息：
  
  三峡金沙江云川水电开发有限公司宁南白鹤滩电厂科研项目;Ningnan Baihetan Hydropower Plant of Three Gorges Jinsha River Yunchuan Hydropower Development Co., Ltd. Research Project(Z532202003)
- DOI：10.13296/j.1001-1609.hva.2026.06.009
  中图分类号：
- 收稿：2025-11-21，
  
  修回：2026-01-27，
  
  纸质出版：2026-06-16
- 稿件说明：
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李利华, 杨新志, 鲍鹏, 等. 基于白化滤波和RBF-KF的变压器局部放电超声波信号提取[J]. 高压电器, 2026,62(6):76-83.

LI Lihua, YANG Xinzhi, BAO Peng, et al. Ultrasonic Signal Extraction of Transformer Partial Discharge Based on Whitening Filter and RBF-KF[J]. High Voltage Apparatus, 2026, 62(6): 76-83.
李利华, 杨新志, 鲍鹏, 等. 基于白化滤波和RBF-KF的变压器局部放电超声波信号提取[J]. 高压电器, 2026,62(6):76-83. DOI： 10.13296/j.1001-1609.hva.2026.06.009.

LI Lihua, YANG Xinzhi, BAO Peng, et al. Ultrasonic Signal Extraction of Transformer Partial Discharge Based on Whitening Filter and RBF-KF[J]. High Voltage Apparatus, 2026, 62(6): 76-83. DOI： 10.13296/j.1001-1609.hva.2026.06.009.

摘要

局部放电（partial discharge，PD）是电力变压器出现绝缘劣化的初始迹象，局部放电信号的及时准确提取对于变压器状态监测具有重要意义。然而，受周期性窄带干扰和白噪声等的影响，传统快速傅里叶变换（fast Fourier transformation，FFT）阈值，小波降噪和奇异值分解（singular value decomposition，SVD）等方法噪声抑制性能较差，难以有效提取PD信息。提出一种基于白化滤波和径向基神经网络联合卡尔曼滤波（radial basis function neural network-Kalman filtering，RBF-KF）的变压器PD超声波信号提取方法。首先，根据周期性窄带干扰特征构建白化滤波矩阵，并利用该矩阵对染噪PD信号进行“白化”预处理，将周期性窄带干扰转化为白噪声。然后，建立卡尔曼滤波（Kalman filter，KF）模型进行白噪声抑制，同时针对KF滤波结果易发散问题，利用径向基神经网络（radial basis function neural network，RBF）对滤波误差进行动态修正，提升噪声抑制性能。基于仿真和实测数据的试验结果表明，所提方法噪声抑制性能优良，相对于传统FFT阈值、小波降噪和SVD方法提取的局部放电超声波信号波形畸变小，能量损失少，具有更高的工程应用前景。

Abstract

Partial discharge (PD) is the initial sign of insulation deterioration in power transformer. The timely and accurate extraction of partial discharge signals is of great significance for transformer status monitoring. However

due to the influence of periodic narrowband interference and white noise

the noise suppression performance of such methods as traditional fast Fourier transformation (FFT) thresholding

wavelet denoising

and singular value decomposition (SVD) methods is are weak and are difficult to effectively extract PD information. In this paper

a transformer PD ultrasonic signal extraction method based on whitening filtering and radial basis function neural network-Kalman filtering (RBF-KF) is proposed. First

a whitening filtering matrix is constructed based on the characteristics of periodic narrowband interference signals

and this matrix is then used to preprocess the noise-cotaminated PD signal through wightening

theteby converting the periodic narrowband interference into white noise.Then

the Kalman filter (KF) model is set up for white noise suppression. Meanwhile

to address the issue that KF filtering results are prone to divergence

a radial basis fucntion neutral network (RBF) is used to dynamically correct the filtering errors

thereby improving the noise suppression performance. The experimental results based on simulation and measured data indicate that the proposed method has excellent noise suppression performance. Compared to traditional FFT threshold

wavelet denoising

and SVD methods

the waveforms of the partial discharge ultrasonic signals extracted by the proposed method have less distortion

less energy loss and greater engineering application prospects.

关键词

Keywords

references

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