Learning to infer: A new variational inference approach for power grid topology identification

Yue Zhao, Jianshu Chen, H. Vincent Poor

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

7 Scopus citations

Abstract

Identifying arbitrary topologies of power networks is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new variational inference approach is developed for efficient marginal inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem. A major advantage of the developed learning based approach is that the labeled data used for learning can be generated in an arbitrarily large amount at very little cost. As a result, the power of offline training is fully exploited to offer effective real-time topology identification. The proposed methods are evaluated in the IEEE 30-bus system. With relatively simple variational models and only an undercomplete measurement set, the proposed method already achieves very good performance in identifying arbitrary power network topologies.

Original languageEnglish (US)
Title of host publication2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PublisherIEEE Computer Society
Volume2016-August
ISBN (Electronic)9781467378024
DOIs
StatePublished - Aug 24 2016
Event19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain
Duration: Jun 25 2016Jun 29 2016

Other

Other19th IEEE Statistical Signal Processing Workshop, SSP 2016
CountrySpain
CityPalma de Mallorca
Period6/25/166/29/16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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  • Cite this

    Zhao, Y., Chen, J., & Poor, H. V. (2016). Learning to infer: A new variational inference approach for power grid topology identification. In 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016 (Vol. 2016-August). [7551827] IEEE Computer Society. https://doi.org/10.1109/SSP.2016.7551827