Machine learning methods for power line outage identification

Abstract

As Phasor Measurement Units (PMUs) become widely deployed, power systems can take advantage of the large amount of data provided by PMUs and leverage the advances in big data analytics to improve real-time monitoring and diagnosis. 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 line outages. The same framework is used for the prediction of both single line and multiple line outages. We investigate a range of machine learning algorithms and feature extraction methods. The algorithms are designed to capture the essential dynamic characteristics of the power system when the topology change occurs abruptly. The proposed methods use only voltage phasor angles obtained by continuously monitoring the buses. We tested the proposed methods on their prediction performance under different levels of noise and missingness. It is shown that the proposed methods have better tolerance for noisy data and incomplete data when compared to the previous work that involves solving power flow equations or state estimation equations.