智能监测及故障诊断技术在电力系统中的应用研究  

Research on Intelligent Monitoring and Fault Diagnosis Technology in Electric Power System

作　　者：胡永恒 张公涛 冯磊 宋其涛 HU Yongheng;ZHANG Gongtao;FENG Lei;SONG Qitao(State Grid Shandong Electric Extrahigh Voltage Company,Jinan 250000,China)

机构地区：[1]国网山东省电力公司超高压公司,山东济南250000

出　　处：《通信电源技术》2024年第18期64-66,共3页Telecom Power Technology

摘　　要：本研究聚焦基于数据驱动的故障检测方法,尤其是主成分分析(Principal Component Analysis,PCA)和支持向量机(Support Vector Machine,SVM)。PCA通过降维提取关键特征,而SVM通过构建超平面实现故障分类。在此基础上重点探讨状态监测与预测维护、故障自愈与系统的恢复的应用,通过模拟实验验证这些措施的有效性。结果表明,这些方法能显著提升故障检测准确性,为电力系统的可靠运行提供有力支持。This research focuses on data-driven fault detection methods,especially Principal Component Analysis(PCA)and Support Vector Machine(SVM).PCA extracts key features by dimensionality reduction,while SVM realizes fault classification by constructing hyperplane.On this basis,the application of condition monitoring and predictive maintenance,fault self-healing and system recovery is discussed emphatically,and the effectiveness of these measures is verified by simulation experiments.The results show that these methods can significantly improve the accuracy of fault detection and provide strong support for the reliable operation of power system.

关 键 词：智能监测 故障诊断 数据分析 机器学习算法 

分 类 号：TM711[电气工程—电力系统及自动化]