基于电力大数据挖掘技术的CVT故障在线监测研究及案例分析  

Research on Online Monitoring of CVT Faults Based on Electric Power Big Data Mining Technology and Case Analysis

作　　者：李永强 LI Yongqiang(Guizhou Chuangxing Electric Power Research Institute Limited Liability Company,Guiyang 550081,China)

机构地区：[1]贵州创星电力科学研究院有限责任公司,贵州贵阳550081

出　　处：《电工技术》2025年第3期20-23,共4页Electric Engineering

摘　　要：研究CVT在线监测技术,不仅可以预防故障、减少运维成本和提高系统可靠性,还可以利用现有资源、数据驱动决策来满足对设备管理的监管需求。利用一种电力大数据挖掘技术,通过监测CVT二次电压变化特征,实现实时监控运行状态,并成功预防一起故障的发生。通过对故障CVT的返厂试验和解剖研究,确定了元件击穿为故障的主要原因,并发现电容元件间引线片边缘毛刺是导致击穿的关键因素。进一步探讨了原材料选择、制造工艺,提出了针对性的改进措施和运行建议,以期为电力系统CVT的可靠性运行提供实践指导。The research on Capacitive Voltage Transformer(CVT)online monitoring technology serves a dual purpose:it not only averts faults and mitigates operation and maintenance expenses but also bolsters system reliability.Furthermore,it harnesses existing resources and leverages data-driven decision-making to fulfill regulatory requirements for equipment management.This manuscript presents a novel approach grounded in electric power big data analytics to achieve real-time monitoring of the operational status of CVT,thereby preemptively averting a fault through vigilant observation of secondary voltage fluctuations.Upon the reversion of a defective CVT to the manufacturer for in-depth testing and dissection,the root cause of the malfunction was ascertained to be a component breakdown.Specifically,the presence of edge burrs on the lead sheets between capacitive elements was identified as the pivotal factor precipitating the failure.The discussion is extended to encompass raw material selection and manufacturing processes,advocating for targeted enhancement measures and operational recommendations.These are aimed at augmenting the dependability of CVT within power systems.

关 键 词：CVT 在线监测 元件击穿 毛刺效应 改进措施 

分 类 号：TM451[电气工程—电器]