Modal Analysis

This project develops a real-time event identification framework using modal and spectral features extracted from wide-area PMU streams. ▣ Problem PMUs produce extremely high-dimensional, high-rate data Operators need rapid recognition of line trips, generator outages, and oscillations SCADA-level alarms do not capture modal characteristics ▣ Approach Extract modal features (frequency, damping, mode shapes) Build a high-dimensional spectral–temporal representation Apply feature selection (LASSO, MI ranking) Train logistic regression and SVM classifiers ▣ Validation Texas 2000-bus synthetic grid Real utility dataset with ≈500 PMUs Strong accuracy and robustness across conditions ▣ Publication IEEE Transactions on Power Systems (2022): https://ieeexplore.ieee.org/document/9911774

A poster contribution demonstrating the use of modal analysis and spectral PMU features for disturbance detection and classification. ▣ Problem Wide-area events leave modal signatures that are not captured by traditional SCADA indicators. ▣ Approach Extract modal dynamics from PMU windows Build feature embeddings for event classification Evaluate temporal stability of modal signatures ▣ Publication IEEE PES General Meeting Poster (2021)