Fault Diagnosis of Auxiliary Components of High Voltage Circuit Breaker Based on Nonlinear State Estimation

CHEN Benbin; SHI Hao; ZHUANG Zhijian

doi:10.13296/j.1001-1609.hva.2020.06.023

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Fault Diagnosis of Auxiliary Components of High Voltage Circuit Breaker Based on Nonlinear State Estimation

更新时间：2025-04-26

- Fault Diagnosis of Auxiliary Components of High Voltage Circuit Breaker Based on Nonlinear State Estimation
- High VoltageApparatus Vol. 56, Issue 6, Pages: 159-164+172(2020)
- 作者机构：
  
  1. 厦门理工学院
  2. ABB(中国)有限公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.023
  CLC： TM561
- Published：2020
- 稿件说明：
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[1]陈本彬,史昊,庄志坚.非线性状态估计的高压断路器辅助元件的故障诊断[J].高压电器,2020,56(06):159-164+172.

CHEN Benbin, SHI Hao, ZHUANG Zhijian. Fault Diagnosis of Auxiliary Components of High Voltage Circuit Breaker Based on Nonlinear State Estimation[J]. High voltageapparatus, 2020, 56(6): 159-164+172.
[1]陈本彬,史昊,庄志坚.非线性状态估计的高压断路器辅助元件的故障诊断[J].高压电器,2020,56(06):159-164+172. DOI： 10.13296/j.1001-1609.hva.2020.06.023.

CHEN Benbin, SHI Hao, ZHUANG Zhijian. Fault Diagnosis of Auxiliary Components of High Voltage Circuit Breaker Based on Nonlinear State Estimation[J]. High voltageapparatus, 2020, 56(6): 159-164+172. DOI： 10.13296/j.1001-1609.hva.2020.06.023.

摘要

针对高压断路器分合闸线圈和储能电机的状态诊断过于复杂的问题

提出一种以易于采集的电流信号来对分合闸线圈和储能电机进行故障诊断。通过非线性状态估计法（nonlinear state estimate technology）分别建立分合闸线圈和储能电机的电流诊断模型

然后根据常见故障类型建立故障阈值

之后根据电流相似度变化趋势进行诊断

实现及早发现分合闸线圈和储能电机的潜在故障。实验结果表明

该方法对分合闸线圈故障诊断正确率达到100%

对储能电机故障诊断正确率达到99.38%。

Abstract

Aiming at the problem that the state diagnosis of high-voltage circuit breaker’s opening and closing coiland energy storage motor is too complicated

a fault diagnosis method with current signal easily collected is proposedfor the opening and closing coil and energy storage motor. The current diagnosis models of the coil and the energystorage motor are established by the Nonlinear State Estimation Technology

and then the fault threshold is estab-lished according to the common fault types. Then the potential faults of the coil and the energy storage motor can bedetected as early as possible by diagnosing the trend of current similarity. The experimental results show that themethod achieves 100% accuracy in fault diagnosis of opening and closing coils

and 99.38% accuracy in fault diagno-sis of energy storage motors.

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