Mechanical Life Prognosis of High Voltage Circuit Breaker Based on Support Vector Machine

HAO Shuang; ZHONG Linlin; WANG Xiaohua; LI Gaoyang; RONG Mingzhe

doi:10.13296/j.1001-1609.hva.2015.07.030

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Mechanical Life Prognosis of High Voltage Circuit Breaker Based on Support Vector Machine

更新时间：2025-04-26

- Mechanical Life Prognosis of High Voltage Circuit Breaker Based on Support Vector Machine
- High VoltageApparatus Vol. 51, Issue 7, Pages: 155-159+165(2015)
- 作者机构：
  
  1. 南方电网科学研究院
  2. 西安交通大学电气工程学院电力设备与电气绝缘国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.07.030
  CLC： TP18;TM561
- Published：2015
- 稿件说明：
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[1]郝爽,仲林林,王小华,李高扬,荣命哲.基于支持向量机的高压断路器机械状态预测算法研究[J].高压电器,2015,51(07):155-159+165.

HAO Shuang, ZHONG Linlin, WANG Xiaohua, et al. Mechanical Life Prognosis of High Voltage Circuit Breaker Based on Support Vector Machine[J]. High voltageapparatus, 2015, 51(7): 155-159+165.
[1]郝爽,仲林林,王小华,李高扬,荣命哲.基于支持向量机的高压断路器机械状态预测算法研究[J].高压电器,2015,51(07):155-159+165. DOI： 10.13296/j.1001-1609.hva.2015.07.030.

HAO Shuang, ZHONG Linlin, WANG Xiaohua, et al. Mechanical Life Prognosis of High Voltage Circuit Breaker Based on Support Vector Machine[J]. High voltageapparatus, 2015, 51(7): 155-159+165. DOI： 10.13296/j.1001-1609.hva.2015.07.030.

摘要

机械故障是高压断路器运行过程中的主要故障之一

对高压断路器开展机械状态评估与预测

对提高高压开关设备和电网运行可靠性具有重要意义。文中基于支持向量机进行了高压断路器机械状态预测算法的研究。支持向量机是一种统计机器学习算法

以结构风险最小化为训练目标

能够很好地解决过学习、维数灾难、局部最优等传统机器学习算法遇到的问题。在具体的算法实现中

文中利用断路器前几次动作的触头行程和操作线圈电流曲线来预测下一次或者后几次动作数据。利用预测出来的机械动作数据对高压断路器进行故障诊断

可以发现高压断路器潜在的问题

从而达到机械状态预测的目的。此外

文中通过归一化、交叉验证、网格搜索等方法来确定算法参数和提高算法精度。最后

以高压断路器机械寿命试验数据为例测试了该算法

结果表明该算法能够很好地训练并预测机械动作行程曲线和操作线圈电流曲线。

Abstract

Mechanical fault is one of the main faults occurring during the life cycle of high-voltage circuit breakers（HVCBs）. In order to enhance the reliability of HVCBs and the power system

it is important to assess and predict the mechanical condition of HVCBs. In this paper

the mechanical prediction algorithm for HVCBs based on support vector machine（SVM）was studied. SVM is a statistical learning algorithm which minimizes the structural risk for training purposes and can solve the problems of traditional machine learning methods（e.g. over-fitting

dimension disaster

local optimum

et al.）. For the implement of algorithm

the historic data of contact travel and coil current were used to predict the future values. In order to predict the mechanical condition

the process of fault diagnosis for HVCBs can be applied. The methods

such as data scale

cross validation and grid search

were adopted to obtain the presetting parameters of algorithm and improve the performance. In the end

the mechanical life experiment data of a HVCB was applied to validate the feasibility of the algorithm. The results showed that the proposed algorithm could predict the mechanical condition of HVCBs successfully.

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