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文章摘要
基于大数据分析和波形匹配算法的配电网缺陷感知模型研究
Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm
Received:November 11, 2022  Revised:December 28, 2022
DOI:10.19753/j.issn1001-1390.2025.04.010
中文关键词: 配电自动化  波形匹配  局放检测  数据挖掘
英文关键词: data analysis, feature extraction, partial discharge detection, neural network
基金项目:国网浙江省电力有限公司双创项目资助B711JZ22006
Author NameAffiliationE-mail
LIN Kaifeng* Jinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd. linfenkai@126.com 
LI Yiming Jinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd. linfenkai@126.com 
ZHANG Bo Jinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd. linfenkai@126.com 
YangChangyu State Grid Zhejiang Jinhua Power Supply CO.,LTD linfenkai@126.com 
ZHU Zeting Jinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd. linfenkai@126.com 
YANGZhenda Jinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd. linfenkai@126.com 
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中文摘要:
      针对配网自动化覆盖后,I区系统中海量信号数据未有效利用和配网环网柜中开关设备在故障前存在频繁瞬时性闪络、接地等情况不能及时发现导致跳闸的问题,文中提出了基于大数据分析和波形匹配算法的配电网缺陷感知模型。利用配电线路的配电终端(distribution terminal unit,DTU)数据采集能力,在不影响馈线自动化(feeder automation,FA)功能的前提下合理设置保护定值,收集故障闪络信息,并对信号波形特征分析以提取故障波形特征用于设备缺陷判别;建立基于波形匹配算法的配网缺陷感知模型,训练学习识别故障波形,采用层次分析算法量化风险品评估;通过配电网缺陷感知系统实例分析证明了设计系统的可行性,指导现场有针对性的局放检测,发现并解决存在演变过程的设备类隐患。
英文摘要:
      After the automatic coverage of the distribution network, the massive signal data in the I area system is not effectively utilized and the switchgear in the distribution site has frequent and instantaneous flashovers and groundings before the fault and cannot be found in time. Distribution network defect sensing system based on waveform matching algorithm. Based on the instantaneous alarm signal and wave recording function of the distribution terminal (DTU) of the distribution line, this paper analyzes the data acquisition principle, waveform analysis and data processing, and finds and solves the existence of evolution through data mining. Equipment hidden dangers in the process; establish a distribution network defect perception model based on big data analysis and waveform matching algorithm, including data format model, convolutional neural network waveform learning model and risk assessment strategy. Finally, the feasibility of the design system is proved through the analysis of the distribution network defect sensing system example, which guides the on-site targeted partial discharge detection, and finds and solves the equipment hidden dangers in the evolution process.
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