This project studies the problem of localizing unknown disturbance sources in large linear dynamical systems using limited measurements. The work is motivated by oscillatory events, faults, and cyber-physical anomalies in power grids.
▣ Problem
- Disturbance inputs propagate through network dynamics
- Only partial measurements of the system state are available
- Need to infer where the excitation originated without full model knowledge
▣ Approach
- Apply subspace system identification to recover reduced-order dynamics
- Formulate a source localization estimator using Markov parameters
- Use structured propagation patterns to recover the most probable source
▣ Validation
- Demonstrated on synthetic linear networks and benchmark system models
- Accurate localization under noise, limited sensors, and model uncertainty
- Applicable to oscillation events and cyber-physical anomalies
▣ Publication
IEEE CCTA 2023:
https://ieeexplore.ieee.org/document/10252510