[1]李永红.高压SF6 气体绝缘开关气室局部放电在线监测研究[J].工业仪表与自动化装置,2025,(01):98-102.[doi:10.19950/j.cnki.CN61-1121/TH.2025.01.018]
　LI Yonghong.Research on online monitoring of partial discharge in the gas chamber of high-voltage SF6 gas insulated switch[J].Industrial Instrumentation & Automation,2025,(01):98-102.[doi:10.19950/j.cnki.CN61-1121/TH.2025.01.018]
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高压SF6 气体绝缘开关气室局部放电在线监测研究()

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2025年01期
页码:
98-102
栏目:
出版日期:
2025-02-15

文章信息/Info

Title:
Research on online monitoring of partial discharge in the gas chamber of high-voltage SF6 gas insulated switch
文章编号:
1000-0682（2025）01-0098-05
作者:
李永红
（河南对外经济贸易职业学院 信息工程学院，河南 开封 475000）
Author(s):
LI Yonghong
(School of Information Engineering, Henan Vocational College of Foreign Economics and Trade, Henan Kaifeng 475000, China)
关键词:
SF6气体绝缘开关气室; 局部放电; 超高频法; 改进小波阈值; 特征参量; 在线监测
Keywords:
SF6 gas insulated switch chamber;  partial discharge;  ultra high frequency method;  improve wavelet threshold;  characteristic parameters;  online monitoring
分类号:
TM591
DOI:
10.19950/j.cnki.CN61-1121/TH.2025.01.018
文献标志码:
A
摘要:
高压SF6气体绝缘开关气室检修过程中，通常利用现场采集数据直接推导局部放电在线监测结果，对噪声信号较为敏感，导致在线监测结果表现出的平均精度(Average Precision，AP)值较低。因此，提出基于改进小波阈值的高压SF6气体绝缘开关气室局部放电在线监测方法。以电平扫描比较原理为基础，设计超高频局部放电监测传感器，输出开关气室放电信号图谱。针对放电在线监测信号进行多尺度小波分解，并引入改进阈值小波阈值算法去除信号中的干扰噪声。以干扰抑制后的测量信号为基础，获取峰度、偏度等特征向量。输入生成对抗网络模型中展开不断学习，获取局部放电在线监测结果。实验结果表明：该方法所得监测结果的AP值达到了0.9，证明了其可以实现开关气室局部放电的准确监测。
Abstract:
During the maintenance process of the high-voltage SF6 gas insulated switch chamber, on-site data collection is usually used to directly derive the online monitoring results of partial discharge, which is prone to interference from low-frequency noise signals, resulting in lower Average Precision(AP) values in the monitoring results. Therefore, a method for online monitoring of partial discharge in high-voltage SF6 gas insulated switch chambers based on improved wavelet threshold is proposed. Based on the principle of level scanning comparison, design an ultra-high frequency partial discharge monitoring sensor and output a switch chamber discharge signal graph. Multi scale wavelet decomposition is applied to discharge online monitoring signals, and an improved threshold wavelet threshold algorithm is introduced to remove interference noise from the signals. Extracting characteristic parameters such as kurtosis, skewness, and interrelationships from the signal spectrum after interference suppression, as the basis for partial discharge monitoring, and continuously learning them into the generative adversarial network model to obtain online partial discharge monitoring results. The experimental results show that the AP value of the monitoring results obtained by this method reaches 0.9, proving that it can achieve accurate monitoring of partial discharge in the switching chamber.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2024-07-04基金项目：河南省职业教育教学改革研究项目：信息技术在平面设计教学中应用的探究（豫教（2019）17184号）第一作者：李永红（1972—），女，河南原阳人，汉族，硕士，高级讲师，主要从事计算机应用技术研究
更新日期/Last Update: 1900-01-01