Topological Machine Learning Methods for Power System Responses to Contingencies

Brian Bush, Yuzhou Chen, Dorcas Ofori-Boateng, Yulia Gel

Research output: Contribution to conferencePaper

4 Scopus Citations


While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remains untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on a neural network architecture for impact metric classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn more about the complex relationships between electrical properties and local topological measurements on their simulated response to contingencies for the NREL-SIIP power system.
Original languageAmerican English
Number of pages8
StatePublished - 2021
EventAAAI Conference on Artificial Intelligence (AAAI-21) -
Duration: 2 Feb 20219 Feb 2021


ConferenceAAAI Conference on Artificial Intelligence (AAAI-21)

Bibliographical note

See NREL/CP-6A20-77899 for preprint

NREL Publication Number

  • NREL/CP-6A20-83109


  • contingency analysis
  • multi-channel deep portfolio networks
  • power systems
  • resilience
  • topological data analysis


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