@inproceedings{c873a7712d8c48bdacf81335ae3522e1,
title = "A machine learning approach for line outage identification in power systems",
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.",
keywords = "Logistic regression, Machine learning, Power systems, Random forest",
author = "Jia He and Cheng, {Maggie X.} and Yixin Fang and Crow, {Mariesa L.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 ; Conference date: 13-09-2018 Through 16-09-2018",
year = "2019",
doi = "10.1007/978-3-030-13709-0_41",
language = "English (US)",
isbn = "9783030137083",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "482--493",
editor = "Giuseppe Nicosia and Giovanni Giuffrida and Giuseppe Nicosia and Panos Pardalos and Vincenzo Sciacca and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers",
address = "Germany",
}