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开关柜内部PVC电缆绝缘材料过热产气的传感器检测方法研究

董驰 庞先海 路士杰 乐相宏 雷芳菲 褚继峰 杨爱军 王小华 荣命哲

全球能源互联网2024,Vol.7Issue(6):726-737,12.
全球能源互联网2024,Vol.7Issue(6):726-737,12.DOI:10.19705/j.cnki.issn2096-5125.2024.06.012

开关柜内部PVC电缆绝缘材料过热产气的传感器检测方法研究

Sensor Detection Method for Overheating and Gas Production of PVC Cable Insulation Material Inside Switchgear

董驰 1庞先海 1路士杰 1乐相宏 2雷芳菲 2褚继峰 2杨爱军 2王小华 2荣命哲2

作者信息

  • 1. 国网河北省电力有限公司电力科学研究院,河北省 石家庄市 050021
  • 2. 西安交通大学电气工程学院电工材料电气绝缘全国重点实验室,陕西省 西安市 710049
  • 折叠

摘要

Abstract

As a crucial component in power systems,cable faults in switchgear can lead to localized power outages.Traditional temperature measurement methods have limitations,such as requiring contact testing,high costs,and blind spots.To address these challenges,proposes a cable overheating detection method for switchgear based on semiconductor gas sensors.First,the study investigates the gas components generated from overheating of polyvinyl chloride(PVC),a material commonly used in cable insulation.Based on these decomposition gases,a gas sensor array is constructed.Then,simulated cable overheating scenarios are conducted to gather response curves from the gas sensors at various temperatures,using threshold values from these curves to identify temperature ranges indicative of overheating.Finally,a cable overheating detection device for switchgear is developed,which can accurately differentiate between overheating states and issue alarms under high-current simulated scenarios within a ventilated hood.

关键词

开关设备/PVC绝缘/过热检测/气体传感器/热分解

Key words

switching equipment/PVC insulation/overheating detection/gas sensor/thermal decomposition

分类

信息技术与安全科学

引用本文复制引用

董驰,庞先海,路士杰,乐相宏,雷芳菲,褚继峰,杨爱军,王小华,荣命哲..开关柜内部PVC电缆绝缘材料过热产气的传感器检测方法研究[J].全球能源互联网,2024,7(6):726-737,12.

基金项目

国网河北省电力有限公司科技项目(基于机器嗅觉感知的高压空气开关柜绝缘故障诊断技术研究,kj2024-032).Science and Technology Project of State Grid Hebei Electric Power Co.,Ltd.(Research on Insulation Fault Diagnosis Technology for High-voltage Air Switchgear Based on Machine Olfactory Perception,kj2024-032). (基于机器嗅觉感知的高压空气开关柜绝缘故障诊断技术研究,kj2024-032)

全球能源互联网

OA北大核心CSTPCD

2096-5125

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