Outage Detection Using Load and Line Flow Measurements in Power Distribution Systems

Raffi Avo Sevlian, Yue Zhao, Ram Rajagopal, Andrea Goldsmith, H. Vincent Poor

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

An outage detection framework for power distribution networks is proposed. Given the tree structure of a distribution system, a detection method is developed combining the use of real-time power flow measurements on the edges of the tree with load forecasts at the nodes of the tree. The maximum likelihood detection decision for an arbitrary number of outages is shown to be efficiently computable due to decoupling across local areas determined by the sensor locations. To minimize the maximum missed detection probability, the optimal sensor placement is efficiently computed as well. Finally, a set of case studies is conducted using feeder data from the Pacific Northwest National Laboratories. It is shown that $10\%$ mean detection error probability can be achieved by a sensor density of $30\%$ for a typical feeder with the proposed optimal sensor placement and outage detection methods.

Original languageEnglish (US)
Pages (from-to)2053-2069
Number of pages17
JournalIEEE Transactions on Power Systems
Volume33
Issue number2
DOIs
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Keywords

  • Demand forecasting
  • SCADA systems
  • forecast uncertainty
  • maximum likelihood detection
  • meter reading
  • power distribution lines
  • power system restoration

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