Free Probability Theory Based Event Detection for Power Grids Using IoT-enabled Measurements  

作　　者：Hongxia Wang Bo Wang Jiaxin Zhang Chengxi Liu Hengrui Ma 

机构地区：[1]Hubei Engineering and Technology Research Center for AC/DC IntelligentDistribution Network,School of Electrical Engineering and Automation,WuhanUniversity,Wuhan,China [2]School of Electrical Engi neering and Automation,Wuhan University,Wuhan,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第5期1396-1407,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported by the National Key Research and Development Program of China(No.2021YFB2401302)。

摘　　要：Taking the advantage of Internet of Things(IoT)enabled measurements,this paper formulates the event detection problem as an information-plus-noise model,and detects events in power systems based on free probability theory(FPT).Using big data collected from phasor measurement units(PMUs),we construct the event detection matrix to reflect both spatial and temporal characteristics of power gird states.The event detection matrix is further described as an information matrix plus a noise matrix,and the essence of event detection is to extract event information from the event detection matrix.By associating the event detection problem with FPT,the empirical spectral distributions(ESDs)related moments of the sample covariance matrix of the information matrix are computed,to distinguish events from“noises”,including normal fluctuations,background noises,and measurement errors.Based on central limit theory(CLT),the alarm threshold is computed using measurements collected in normal states.Additionally,with the aid of sliding window,this paper builds an event detection architecture to reflect power grid state and detect events online.Case studies with simulated data from Anhui,China,and real PMU data from Guangdong,China,verify the effectiveness of the proposed method.Compared with other data-driven methods,the proposed method is more sensitive and has better adaptability to the normal fluctuations,background noises,and measurement errors in real PMU cases.In addition,it does not require large number of training samples as needed in the training-testing paradigm.

关 键 词：Big data event detection empirical spectral distribution(ESD) free probability theory(FPT) information-plus-noise model Internet of Things(IoT) phasor measurement unit(PMU) 

分 类 号：TM73[电气工程—电力系统及自动化] TN929.5[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]