TY - GEN
T1 - Comparative Analysis of State and Parameter Estimation Techniques for Power System Frequency Dynamics
AU - Poudel, Bidur
AU - Aslami, Pooja
AU - Aryal, Tara
AU - Bhujel, Niranjan
AU - Rai, Astha
AU - Rauniyar, Manisha
AU - Rekabdarkolaee, Hossein Moradi
AU - Tamrakar, Ujjwol
AU - Hansen, Timothy M.
AU - Tonkoski, Reinaldo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Dynamic state and parameter estimation in current and future power systems are critical for advanced monitoring, control, and protection. There are numerous methods to perform dynamic state and parameter estimation; this paper compares the accuracy and computational time of four methods (i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and moving horizon estimation (MHE)) designed to estimate the states and parameters for frequency dynamics of a power system. A simulation study was conducted using Matlab/Simulink by introducing Gaussian and non-Gaussian noise in the measurements. Results under Gaussian noise showed similar accuracy performance for all filters. EKF and UKF presented convergence or numerical instability issues due to incorrect initial guesses of parameters. MHE did not present convergence issues, however, required comparatively higher computation time. Nonetheless, the MHE could still be implemented in real-time for state and parameter estimation of power system. The impact of non-Gaussian noise on the methods was inconclusive and will require further study.
AB - Dynamic state and parameter estimation in current and future power systems are critical for advanced monitoring, control, and protection. There are numerous methods to perform dynamic state and parameter estimation; this paper compares the accuracy and computational time of four methods (i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and moving horizon estimation (MHE)) designed to estimate the states and parameters for frequency dynamics of a power system. A simulation study was conducted using Matlab/Simulink by introducing Gaussian and non-Gaussian noise in the measurements. Results under Gaussian noise showed similar accuracy performance for all filters. EKF and UKF presented convergence or numerical instability issues due to incorrect initial guesses of parameters. MHE did not present convergence issues, however, required comparatively higher computation time. Nonetheless, the MHE could still be implemented in real-time for state and parameter estimation of power system. The impact of non-Gaussian noise on the methods was inconclusive and will require further study.
KW - Computational tractability
KW - Kalman filter
KW - extended Kalman filter
KW - moving horizon estimation
KW - state and parameter estimation
KW - unscented Kalman filter
UR - http://www.scopus.com/inward/record.url?scp=85136178787&partnerID=8YFLogxK
U2 - 10.1109/SPEEDAM53979.2022.9842272
DO - 10.1109/SPEEDAM53979.2022.9842272
M3 - Conference contribution
AN - SCOPUS:85136178787
T3 - 2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2022
SP - 754
EP - 761
BT - 2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2022
Y2 - 22 June 2022 through 24 June 2022
ER -