Adaptive Distributed Graph Model for Multiple-Line Outage Identification in Large-Scale Power System

Huayi Wu, Zhao Xu, Youwei Jia, Xu Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The real-time outage identification and localization of a potentially large number of transmission line outages is of vital importance while fairly challenging under limited measurement resources. To address this issue, an adaptive distributed graph model (ADGM) is innovatively proposed for multiple-line outage identification to hedge limited measurement and noise in the large-scale power system. By integrating a novel Laplacian convolution (LC) operation, the proposed ADGM is forceful in capturing the non-Euclidian structure of nodal voltage phase angle measurement to tackle the real-time outage identification problem effectively with measurement noise. On top of this, a novel breadth walk (BW) operation is proposed to exclude redundant measurement so that enhanced outage identification accuracy can be achieved under measurement lost. BW is then incorporated with LC to release the ADGM from numerous parameters' training burden to achieve the large-scale system outage identification. Numerical simulations are carried out based on the IEEE 30/118/300-node and Polish 2383-node testing systems, which verify the effectiveness, efficiency, and robustness of the proposed model.

Original languageEnglish
Pages (from-to)3127
Number of pages3137
JournalIEEE Systems Journal
Issue number2
Publication statusPublished - Jun 2023


  • Breadth walk (BW)
  • Convolution
  • Laplace equations
  • laplacian convolution (LC) operation
  • Mathematical models
  • multiple-line outage identification
  • Power systems
  • Topology
  • Transmission line measurements
  • Voltage measurement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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