Research on Transformer Fault Diagnosis Method Based on Vibration Noise and BP Neural Network

YU Zhangting; LI Dajian; JI Shengchang; DENG Jun

doi:10.13296/j.1001-1609.hva.2020.06.037

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Research on Transformer Fault Diagnosis Method Based on Vibration Noise and BP Neural Network

更新时间：2025-04-26

- Research on Transformer Fault Diagnosis Method Based on Vibration Noise and BP Neural Network
- High VoltageApparatus Vol. 56, Issue 6, Pages: 256-261(2020)
- 作者机构：
  
  1. 广西电网有限责任公司电力科学研究院
  2. 西安交通大学电气工程学院
  3. 中国南方电网有限责任公司超高压输电公司检修试验中心
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.037
  CLC： TM407
- Published：2020
- 稿件说明：
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[1]余长厅,黎大健,汲胜昌,邓军.基于振动噪声及BP神经网络的变压器故障诊断方法研究[J].高压电器,2020,56(06):256-261.

YU Zhangting, LI Dajian, JI Shengchang, et al. Research on Transformer Fault Diagnosis Method Based on Vibration Noise and BP Neural Network[J]. High voltageapparatus, 2020, 56(6): 256-261.
[1]余长厅,黎大健,汲胜昌,邓军.基于振动噪声及BP神经网络的变压器故障诊断方法研究[J].高压电器,2020,56(06):256-261. DOI： 10.13296/j.1001-1609.hva.2020.06.037.

YU Zhangting, LI Dajian, JI Shengchang, et al. Research on Transformer Fault Diagnosis Method Based on Vibration Noise and BP Neural Network[J]. High voltageapparatus, 2020, 56(6): 256-261. DOI： 10.13296/j.1001-1609.hva.2020.06.037.

摘要

文中提出了基于变压器振动噪声及BP神经网络的故障诊断方法

通过振动噪声检测系统获得变压器振动噪声信号

经FFT变换计算得到特征值

特征值作为输入量经训练好了的BP神经网络预测得到变压器故障类型。通过对6种变压器典型形态试验的诊断

验证了该方法的有效性。该方法充分利用变压器振动噪声信号

通过BP神经网络算法实现变压器带电故障诊断

大大提高了变压器故障诊断率

为变压器运维人员提供了一种带电巡检的有效途径。

Abstract

In this paper

a fault diagnosis method based on the transformer vibration noise and BP neural network isproposed. The transformer vibration noise signal is obtained by the vibration noise detection system

and the eigen-value is calculated by FFT. The eigenvalue is used as the input of the trained BP neural network and the type of trans-former fault is predicted. The validity of the method is verified by the diagnosis of typical state test of 6 kinds of trans-formers. This method makes full use of the vibration and noise signal of transformer

and realizes the diagnosis oftransformer live fault by BP neural network algorithm. It greatly improves the fault diagnosis rate of the transformerand provides an effective way for the transformer live inspection.

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