Control

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

This project applies adaptive sliding-mode control to virtual synchronous generators (VSGs) to improve microgrid frequency response and damping. ▣ Problem Converter-dominated microgrids lack physical inertia VSGs emulate synchronous behavior but need robust control Frequency excursions and oscillations persist under disturbances ▣ Approach Design an adaptive sliding-mode VSG controller Enhance frequency response and damping under varying conditions Evaluate robustness against load changes and disturbances ▣ Publication IEEE EPE (2018): https://ieeexplore.ieee.org/document/8559616