A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data

TL;DR

This paper presents a machine learning framework that uses modal analysis of PMU data to accurately identify power system events like line trips and generation loss, combining physics-based features with classification models.

Contribution

It introduces a novel approach integrating modal analysis with feature selection and machine learning for real-time event identification in power systems.

Findings

Effective feature selection improves classification accuracy.

Support vector machines outperform logistic regression in this task.

Framework successfully identifies events in both simulated and real datasets.

Abstract

Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. To address this issue, various feature selection methods are implemented to choose the best subset of features. Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. Our results indicate that the proposed framework is promising for identifying the two types of events.