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