A machine learning approach for line outage identification in power systems

Jia He, Maggie X. Cheng, Yixin Fang, Mariesa L. Crow

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

Abstract

This paper addresses power line topology change detection by using only measurement data. As Phasor Measurement Units (PMUs) become widely deployed, power system monitoring and real-time analysis can take advantage of the large amount of data provided by PMUs and leverage the advances in big data analytics. In this paper, we develop practical analytics that are not tightly coupled with the power flow analysis and state estimation, as these tasks require detailed and accurate information about the power system. We focus on power line outage identification, and use a machine learning framework to locate the outage(s). The same framework is used for both single line outage identification and multiple line outage identification. We first compute the features that are essential to capture the dynamic characteristics of the power system when the topology change happens, transform the time-domain data to frequency-domain, and then train the algorithms for the prediction of line outage based on frequency domain features. The proposed method uses only voltage phasor angles obtained by continuous monitoring of buses. The proposed method is tested by simulated PMU data from PSAT [1], and the prediction accuracy is comparable to the previous work that involves solving power flow equations or state estimation equations.

Original languageEnglish (US)
Title of host publicationMachine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers
EditorsGiuseppe Nicosia, Giovanni Giuffrida, Panos Pardalos, Vincenzo Sciacca, Renato Umeton
PublisherSpringer Verlag
Pages482-493
Number of pages12
ISBN (Print)9783030137083
DOIs
StatePublished - Jan 1 2019
Event4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 - Volterra, Italy
Duration: Sep 13 2018Sep 16 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11331 LNCS

Conference

Conference4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018
CountryItaly
CityVolterra
Period9/13/189/16/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Logistic regression
  • Machine learning
  • Power systems
  • Random forest

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  • Cite this

    He, J., Cheng, M. X., Fang, Y., & Crow, M. L. (2019). A machine learning approach for line outage identification in power systems. In G. Nicosia, G. Giuffrida, P. Pardalos, V. Sciacca, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers (pp. 482-493). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11331 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-13709-0_41