Topological Machine Learning Methods for Power System Responses to Contingencies

Authors

  • Brian Bush National Renewable Energy Laboratory
  • Yuzhou Chen Southern Methodist University Lawrence Berkeley National Laboratory
  • Dorcas Ofori-Boateng Portland State University
  • Yulia R. Gel University of Texas at Dallas Lawrence Berkeley National Laboratory

Keywords:

Topological Data Analysis, Power Systems, Contingency Analysis, Resilience, Multi-channel Deep Portfolio Networks

Abstract

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.

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Published

2021-05-18

How to Cite

Bush, B., Chen, Y., Ofori-Boateng, D., & Gel, Y. R. (2021). Topological Machine Learning Methods for Power System Responses to Contingencies. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15262-15269. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17791

Issue

Section

IAAI Technical Track on Emerging Applications of AI