A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

Yue Zhao, Jianshu Chen, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new 'Learning-to-Infer' method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multi-line outage identification. The proposed methods are evaluated in the IEEE 30, 118, and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data.

Original languageEnglish (US)
Article number8747534
Pages (from-to)555-564
Number of pages10
JournalIEEE Transactions on Smart Grid
Issue number1
StatePublished - Jan 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science


  • Line outage detection
  • Monte Carlo method
  • machine learning
  • power system monitoring
  • variational inference


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