Application of Neural Network Ensemble Model in Mechanical Fault Identification of High Voltage Circuit Breaker

ZHAO Ke; YANG Jinggang; MA Suliang; WANG Yuhao; WU Jianwen; LIANG Chuantao

doi:10.13296/j.1001-1609.hva.2018.07.030

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Application of Neural Network Ensemble Model in Mechanical Fault Identification of High Voltage Circuit Breaker

更新时间：2025-04-26

- Application of Neural Network Ensemble Model in Mechanical Fault Identification of High Voltage Circuit Breaker
- High VoltageApparatus Vol. 54, Issue 7, Pages: 217-223(2018)
- 作者机构：
  
  1. 国网江苏省电力公司电力科学研究院
  2. 北京航空航天大学自动化科学与电气工程学院
  3. 山东泰开高压开关有限公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.07.030
  CLC： TM561
- Published：2018
- 稿件说明：
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[1]赵科,杨景刚,马速良,王昱皓,武建文,梁传涛.基于神经网络集成模型在高压断路器机械故障诊断中的应用[J].高压电器,2018,54(07):217-223.

ZHAO Ke, YANG Jinggang, MA Suliang, et al. Application of Neural Network Ensemble Model in Mechanical Fault Identification of High Voltage Circuit Breaker[J]. High voltageapparatus, 2018, 54(7): 217-223.
[1]赵科,杨景刚,马速良,王昱皓,武建文,梁传涛.基于神经网络集成模型在高压断路器机械故障诊断中的应用[J].高压电器,2018,54(07):217-223. DOI： 10.13296/j.1001-1609.hva.2018.07.030.

ZHAO Ke, YANG Jinggang, MA Suliang, et al. Application of Neural Network Ensemble Model in Mechanical Fault Identification of High Voltage Circuit Breaker[J]. High voltageapparatus, 2018, 54(7): 217-223. DOI： 10.13296/j.1001-1609.hva.2018.07.030.

摘要

高压断路器健康情况严重影响着电网的安全、稳定运行。文中提出一种基于高压断路器合闸过程振动信号时频特征下的集成学习神经网络模型

满足高压断路器故障情况高精度诊断的要求。首先

分析高压断路器在多测量位置下合闸振动信号特征

并在时、频两域定义合闸过程多测量位置振动信号的广义能量和小波能量比进行特征空间描述;然后

设计基于集成学习思想的神经网络算法划分特征空间

诊断故障类别;最后

通过实验数据分析和多种诊断方法的对比验证文中所述的诊断过程合理、诊断结果精确

有利于高压断路器故障排查。

Abstract

The health of high voltage circuit breaker seriously affects the safe and stable operation of power grid. Inthis paper

a neural network ensemble model based on time-frequency characteristics of vibration information of high-voltage circuit breaker is proposed in view of the requirement of high-precision circuit breaker fault diagnosis. First-ly

the characteristics of the vibration signal of the high-voltage circuit breaker under the multi-measurement posi-tion are analyzed. The generalized energy of the vibration signal and the wavelet energy ratio are described in the fea-ture space. Then

neural network ensemble model is used to divide the feature space

and the fault category is diag-nosed. Finally

by comparing the experimental data with various diagnostic methods

it is verified that the diagnosisprocess described in this article is reasonable and the diagnosis result is accurate

and it is beneficial to the trouble-shooting of high voltage circuit breakers.

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