TY - JOUR
T1 - Oscillation Parameter Estimation via State-Space Modeling of Synchrophasors
AU - Lee, Yonggu
AU - Lee, Gyul
AU - White, Austin
AU - Shin, Yong June
N1 - Publisher Copyright:
© 1969-2012 IEEE.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - This paper presents a technique for the estimation of oscillation parameters via state-space modeling of synchrophasor data. Due to the spectral leakage of synchrophasors estimated by a Fourier transform-based algorithm, the oscillation parameters such as magnitude and frequency will be inherently distorted. Therefore, a state-space model of the instantaneous waveform signal under power system oscillation is derived as an exponentially damped sinusoidal (EDS) signal in order to account for the spectral leakage. The oscillation frequency and magnitude are estimated in real-time by using an unscented Kalman filter (UKF) based on the state-space model. The estimation accuracy performance is validated using the simulation data of sub-synchronous oscillation (SSO). In addition, the efficacy of the proposed method's performance is verified by the application of the proposed method to real-world oscillation events in a wind farm and comparison with the time-frequency analysis.
AB - This paper presents a technique for the estimation of oscillation parameters via state-space modeling of synchrophasor data. Due to the spectral leakage of synchrophasors estimated by a Fourier transform-based algorithm, the oscillation parameters such as magnitude and frequency will be inherently distorted. Therefore, a state-space model of the instantaneous waveform signal under power system oscillation is derived as an exponentially damped sinusoidal (EDS) signal in order to account for the spectral leakage. The oscillation frequency and magnitude are estimated in real-time by using an unscented Kalman filter (UKF) based on the state-space model. The estimation accuracy performance is validated using the simulation data of sub-synchronous oscillation (SSO). In addition, the efficacy of the proposed method's performance is verified by the application of the proposed method to real-world oscillation events in a wind farm and comparison with the time-frequency analysis.
KW - Exponentially damped sinusoidal (EDS) signal
KW - oscillation parameter estimation
KW - short-time Fourier transform (STFT)
KW - state-space modeling
KW - sub-synchronous oscillation (SSO)
KW - synchrophasor
KW - unscented Kalman filter (UKF)
KW - wind turbine generator
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U2 - 10.1109/TPWRS.2023.3332248
DO - 10.1109/TPWRS.2023.3332248
M3 - Article
AN - SCOPUS:85177071381
SN - 0885-8950
VL - 39
SP - 5219
EP - 5228
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 3
ER -