TY - JOUR
T1 - An Unscented Particle Filtering Approach to Decentralized Dynamic State Estimation for DFIG Wind Turbines in Multi-Area Power Systems
AU - Yu, Samson Shenglong
AU - Guo, Junhao
AU - Chau, Tat Kei
AU - Fernando, Tyrone
AU - Iu, Herbert Ho Ching
AU - Trinh, Hieu
PY - 2020/7
Y1 - 2020/7
N2 - This paper introduces a novel application of a stochastic filtering algorithm-unscented particle filter (UPF)-to estimate the inaccessible state variables of doubly fed induction generator (DFIG) connected to a multi-area power system with local phasor measurement units (PMUs). This dynamic estimation implementation bears more advanced features than the particle filter (PF) method since it can not only track the dynamic states more accurately and smoothly when the power system experiences sudden disturbances, but also manage to resolve the particle degeneration problem that exists in the PF algorithm. Moreover, the proposed UPF-based dynamic state estimation method is achieved in a decentralized manner and only uses local PMU measurements of voltage and current. Through a comparison study where popular stochastic filtering methods, unscented Kalman filter (UKF) and PF, are employed to achieve the same estimation purpose, this paper shows the superiority of the UPF algorithm particularly designed for state estimation over the other two existing algorithms in terms of accuracy and error tolerance.
AB - This paper introduces a novel application of a stochastic filtering algorithm-unscented particle filter (UPF)-to estimate the inaccessible state variables of doubly fed induction generator (DFIG) connected to a multi-area power system with local phasor measurement units (PMUs). This dynamic estimation implementation bears more advanced features than the particle filter (PF) method since it can not only track the dynamic states more accurately and smoothly when the power system experiences sudden disturbances, but also manage to resolve the particle degeneration problem that exists in the PF algorithm. Moreover, the proposed UPF-based dynamic state estimation method is achieved in a decentralized manner and only uses local PMU measurements of voltage and current. Through a comparison study where popular stochastic filtering methods, unscented Kalman filter (UKF) and PF, are employed to achieve the same estimation purpose, this paper shows the superiority of the UPF algorithm particularly designed for state estimation over the other two existing algorithms in terms of accuracy and error tolerance.
KW - Doubly fed induction generator
KW - dynamic state estimation
KW - particle filter
KW - phasor measurement units
KW - unscented Kalman filter
KW - unscented particle filter
UR - http://www.scopus.com/inward/record.url?scp=85086888827&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2020.2966443
DO - 10.1109/TPWRS.2020.2966443
M3 - Article
AN - SCOPUS:85086888827
VL - 35
SP - 2670
EP - 2682
JO - IEEE Transactions on Power System
JF - IEEE Transactions on Power System
SN - 0885-8950
IS - 4
M1 - 8959182
ER -