Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real-time. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data) in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. This paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. Our results indicate that the proposed framework is promising for identifying the two types of events. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. 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. To address this issue, various feature selection methods are implemented to choose the best subset of features. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Power systems are prone to a variety of events (e.g.
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