Kai Sun, professor of electrical engineering and computer science (EECS) and an Institute of Electrical and Electronics Engineers (IEEE) Fellow, along with EECS students Xin Xu, Wenyun Ju, and Bin Wang, have been awarded one of the five IEEE Transactions on Power Systems Outstanding Papers for 2023.
The outstanding papers are chosen from candidate papers nominated by associate editors and published in Transactions on Power Systems (TPWRS) during the past three years. Criteria for the selected papers include, “level of novelty and contributions of the paper” and “impacts of the paper on the TPWRS community.” Certificates are presented at the TPWRS Outstanding Paper Recognition Ceremony.
TPWRS has an editorial philosophy which promotes research, innovation, and exchange for the power engineering community. The TPWRS editorial board publishes papers on the “education, analysis, operation, planning, and economics of electric generation, transmission, and distribution systems for general industrial, commercial, public, and domestic consumption, including the interaction with multi-energy carriers.”
The paper, co-authored by Sun and the EE students, is entitled, “Real-time Damping Estimation on Nonlinear Electromechanical Oscillation.” It focuses on their research identifying a nonlinear oscillator to fit a dominant mode. Real-time damping estimation for a dominant mode is important for bringing awareness to potential angular instability in power systems.
Electromechanical oscillations energized by large disturbances often reveal nonlinearities in measurements on the first few swings. Traditional methods based on linear system theory usually discard those first few swings to avoid nonlinearity. If they were not discarded, the estimated damping ratios would vary with the length and starting point of the measuring window.
Thus, this paper proposes “a new measurement-based approach utilizing complete post-disturbance data for robust damping estimation independent of the measuring window.” Based on case studies on the IEEE 9-bus system and a 48-machine Northeast Power Coordinating Council system, the proposed approach validates the usage of accurate and robust damping estimation compared to existing methods such as the Prony’s method.
The paper also addresses three factors that influence damping estimation: measurement noises, limited coverage of PMU measurements, and existence of multiple dominant modes.
Below is the full citation for the paper:
Xin Xu, Wenyun Ju, Bin Wang, Kai Sun, “Real-time Damping Estimation on Nonlinear Electromechanical Oscillation,” IEEE Transactions on Power Systems, vol. 36, No. 4, pp. 3142-3152, July 2021.