TY - GEN
T1 - Dynamic State Estimation for DFIG Wind Turbine with Stochastic Wind Speed in Power System
AU - Su, Wuyang
AU - Liu, Bin
AU - Li, Zhen
AU - Mao, Xuefei
AU - Huang, Meng
AU - He, Guoqing
AU - Liu, Xiangdong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/26
Y1 - 2018/4/26
N2 - The reliable operation of doubly-fed induction generator (DFIG) wind turbine (WT) systems rely on the accurate information of states. However, due to the unavailability of some states from phasor measurement units (PMUs), dynamic state estimation (DSE) for DFIG-WT connecting to the power system becomes essential. Although various DSEs have been applied, the variable stochastic wind speed was excluded into consideration, leading to the inaccurate estimation results. This paper develops the DSE using centralized Kalman filter (CKF) for DFIG-WT under the stochastic wind speed. The wind speed is modeled by stochastic differential equations (SDE), which can generate the trajectories with statistical properties similar to the wind speed historical data available for a particular location, so that the variable wind speed can be applied to the filtering process. Finally, the system involving a DFIG connected to a standard IEEE 14-bus system is utilized to verify the feasibility of the proposed method with the occurrence of electric faults.
AB - The reliable operation of doubly-fed induction generator (DFIG) wind turbine (WT) systems rely on the accurate information of states. However, due to the unavailability of some states from phasor measurement units (PMUs), dynamic state estimation (DSE) for DFIG-WT connecting to the power system becomes essential. Although various DSEs have been applied, the variable stochastic wind speed was excluded into consideration, leading to the inaccurate estimation results. This paper develops the DSE using centralized Kalman filter (CKF) for DFIG-WT under the stochastic wind speed. The wind speed is modeled by stochastic differential equations (SDE), which can generate the trajectories with statistical properties similar to the wind speed historical data available for a particular location, so that the variable wind speed can be applied to the filtering process. Finally, the system involving a DFIG connected to a standard IEEE 14-bus system is utilized to verify the feasibility of the proposed method with the occurrence of electric faults.
KW - Doubly fed induction generator (DFIG)
KW - centralized Kalman filter (CKF)
KW - dynamic state estimation (DSE)
KW - stochastic wind speed
UR - http://www.scopus.com/inward/record.url?scp=85057137157&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2018.8351791
DO - 10.1109/ISCAS.2018.8351791
M3 - Conference contribution
AN - SCOPUS:85057137157
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Y2 - 27 May 2018 through 30 May 2018
ER -