广域测量数据下的电力系统异常事件检测与识别方法研究  

Research on Detection and Identification Methods of Abnormal Events in Power Systems Under Wide Area Measurement Data

作　　者：何小良 张心怡 HE Xiaoliang;ZHANG Xinyi(State Grid Hangzhou Power Supply Company,Hangzhou 310000,China)

机构地区：[1]国网杭州供电公司,浙江杭州310000

出　　处：《电工技术》2025年第5期37-39,共3页Electric Engineering

摘　　要：针对电力系统中异常事件检测与识别的问题,提出了基于广域测量数据的方法。采用不等距压缩算法对相量测量单元数据进行压缩,结合局部离群因子技术实现电力系统事件检测。同时,提出基于模糊等价关系聚类的发电机同调识别方法,引入10个同调性指标全面描述发电机组的同调特性。通过WECC179节点系统的仿真分析,表明基于局部离群因子的事件检测方法成功识别出6个模拟故障中的关键节点,并准确定位故障位置;基于模糊等价关系聚类方法在固有振荡事件中将29台发电机准确划分为2个同调机群,为电力系统的稳定性分析和控制提供了重要支持。This study introduces an innovative method utilizing wide-area measurement data to detect and identify abnormal events in power systems.The proposed approach compresses phasor measurement unit(PMU)data using an unequal distance compression algorithm and employs local outlier factor(LOF)technology for event detection.Additionally,a generator synchronization identification method based on fuzzy equivalence relation(FER)clustering is introduced,with ten synchronization indicators describing generator set characteristics.Simulation analysis of the WECC179 node system confirms that the proposed method effectively detects abnormal power system events,identifies synchronous generator groups,and accurately locates faults,offering crucial support for stability analysis and control.

关 键 词：广域测量系统 异常事件检测 数据压缩 局部离群因子 

分 类 号：TM711[电气工程—电力系统及自动化]