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作 者:Yizheng Liao Yang Weng Cin-Woo Tan Ram Rajagopal
机构地区:[1]Department of Civil and Environmental Engineering,Stanford University,Stanford,CA,94305,USA [2]School of Electrical,Computing,and Energy Engineering,Arizona State University,Tempe,AZ,85287,USA
出 处:《CSEE Journal of Power and Energy Systems》2022年第4期1074-1086,共13页中国电机工程学会电力与能源系统学报(英文)
摘 要:The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.
关 键 词:Power distribution network outage detection outage identification voltage measurement change point detection graphical model
分 类 号:TM76[电气工程—电力系统及自动化]
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