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UID:20260523T235547EDT-3051zvDLWK@132.216.98.100
DTSTAMP:20260524T035547Z
DESCRIPTION:Abstract\n\nForced oscillations (FO)\, resulting from periodic 
 external disturbances\, pose significant threats to the security and stabi
 lity of the power system. The increasing penetration of Inverter-Based Res
 ources (IBRs) introduces additional challenges in identifying and locating
  these oscillation sources. To address this issue\, this thesis proposes a
  novel\, purely data-driven method that is based on sparse identification 
 of nonlinear dynamics (SINDy) for online forced oscillation source locatio
 n. Unlike previous approaches\, a unified representation of forced oscilla
 tions originating from IBRs is considered\, enhancing the algorithm's effe
 ctiveness in modern power systems. The proposed method requires no model i
 nformation and operates with minimal tuning and low computational cost\, m
 aking it viable for online applications. Extensive validations on both sim
 ulated cases (WECC 179-bus and 240-bus systems) and actual oscillation eve
 nts (ISO New England system from the IEEE Task Force test cases library) d
 emonstrate the algorithm's capability to accurately identify oscillation s
 ources\, even under resonance conditions and poorly damped natural modes. 
 The results confirm the robustness and suitability of the method for real-
 world power system applications.\n
DTSTART:20251104T180000Z
DTEND:20251104T200000Z
LOCATION:Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H
 3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Yao Jie Cai – Locate Power System Forced Oscillation
 s based on Sparse Identification of Nonlinear Dynamics
URL:/ece/channels/event/phd-defence-yao-jie-cai-locate
 -power-system-forced-oscillations-based-sparse-identification-368679
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