Efficient neural network architecture for topology identification in smart grid

Yue Zhao, Jianshu Chen, H. Vincent Poor

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

4 Scopus citations

Abstract

Identifying arbitrary power grid topologies in real time based on measurements in the grid is studied. A learning based approach is developed: binary classifiers are trained to approximate the maximum a-posteriori probability (MAP) detectors that each identifies the status of a distinct line. An efficient neural network architecture in which features are shared for inferences of all line statuses is developed. This architecture enjoys a significant computational complexity advantage in the training and testing processes. The developed classifiers based on neural networks are evaluated in the IEEE 30-bus system. It is demonstrated that, using the proposed feature sharing neural network architecture, a) the training and testing times are drastically reduced compared with training a separate neural network for each line status inference, and b) a small amount of training data is sufficient for achieving a very good real-time topology identification performance.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages811-815
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

Keywords

  • Cascading failures
  • Line outage detection
  • Machine learning
  • Neural networks
  • Online power grid topology identification

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  • Cite this

    Zhao, Y., Chen, J., & Poor, H. V. (2017). Efficient neural network architecture for topology identification in smart grid. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 811-815). [7905955] (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7905955