LEARNING to INFER POWER GRID TOPOLOGIES: PERFORMANCE and SCALABILITY

Yue Zhao, Jianshu Chen, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A 'Learning-to-Infer' variational inference method is employed for efficient inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. As the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification. The Learning-to-Infer method is extensively evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance and scalability of the method in identifying arbitrary power network topologies in real time are demonstrated.

Original languageEnglish (US)
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages215-219
Number of pages5
ISBN (Print)9781538644102
DOIs
StatePublished - Aug 17 2018
Event2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland
Duration: Jun 4 2018Jun 6 2018

Publication series

Name2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Other

Other2018 IEEE Data Science Workshop, DSW 2018
CountrySwitzerland
CityLausanne
Period6/4/186/6/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology
  • Control and Optimization

Keywords

  • cascading failures
  • line outage detection
  • machine learning
  • power grid topology identification

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