Study on State Evaluation of Partial Discharge in High Voltage Switchgear Based on Fuzzy Clustering Algorithm

XIE Jing; SHU Hongchun; WANG Ke; SONG Qi

doi:10.13296/j.1001-1609.hva.2015.010.013

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Study on State Evaluation of Partial Discharge in High Voltage Switchgear Based on Fuzzy Clustering Algorithm

更新时间：2025-04-26

- Study on State Evaluation of Partial Discharge in High Voltage Switchgear Based on Fuzzy Clustering Algorithm
- High VoltageApparatus Vol. 51, Issue 10, Pages: 82-90+96(2015)
- 作者机构：
  
  1. 昆明理工大学电力学院
  2. 云南省电力试验研究院(集团)有限公司电力研究院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.010.013
  CLC： TM591
- Published：2015
- 稿件说明：
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[1]谢静,束洪春,王科,宋琪.基于FCM算法的高压开关柜局部放电状态评价方法研究[J].高压电器,2015,51(10):82-90+96.

XIE Jing, SHU Hongchun, WANG Ke, et al. Study on State Evaluation of Partial Discharge in High Voltage Switchgear Based on Fuzzy Clustering Algorithm[J]. High voltageapparatus, 2015, 51(10): 82-90+96.
[1]谢静,束洪春,王科,宋琪.基于FCM算法的高压开关柜局部放电状态评价方法研究[J].高压电器,2015,51(10):82-90+96. DOI： 10.13296/j.1001-1609.hva.2015.010.013.

XIE Jing, SHU Hongchun, WANG Ke, et al. Study on State Evaluation of Partial Discharge in High Voltage Switchgear Based on Fuzzy Clustering Algorithm[J]. High voltageapparatus, 2015, 51(10): 82-90+96. DOI： 10.13296/j.1001-1609.hva.2015.010.013.

摘要

开关柜作为配电网络中应用最广泛

使用数量最多的配电设备之一

其可靠性关系到整个电网的安全稳定运行。文中提出了一种适用于高压开关柜状态评价的模糊聚类方法

为减小周围电磁环境对评价结果带来的干扰

对带电检测数据采用突变量输入

利用经验定值对传统模糊聚类算法中的可变聚类中心进行修正

使得聚类结果更适用于开关柜状态现场实际情况

并使得各次聚类评价结果间具有可比性

能够做到不需要先验信息直接对样本进行聚类

通过实例分析验证了评价方法的有效性

利用现场数据实算验证了方法的可行性。

Abstract

Switchgears as the most widely used power equipment in distribution network

their reliability is related to the safe and stable operation of power grid. Fuzzy clustering algorithm is proposed for a high voltage switchgear state evaluation

the data of on-live detection using sudden-change component in order to reduce the disturbance to the surrounding electromagnetic environment is accepted. In order to make the evaluation results have comparability

the algorithm uses invariant center for clustering method

and it can directly cluster sample without prior information. The feasibility and effectiveness of the algorithm is verified by real data collected in substation.

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