State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems

This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power systems 2016-11, Vol.31 (6), p.4935-4944
Main Authors: Shenglong Yu, Emami, Kianoush, Fernando, Tyrone, Iu, Herbert H. C., Kit Po Wong
Format: Article
Language:English
Subjects:
Bad data detection
doubly fed induction generator (DFIG)
Dynamical systems
Dynamics
EKF
Electric power systems
Error detection
Induction generators
Mathematical model
PMU
Power supply
Power system dynamics
Rotors
State estimation
Stators
Tolerances
UKF
Wind turbines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is performed using the Unscented Kalman Filter (UKF) with a bad data detection scheme. Such a state estimation scheme for a DFIG is important because not all dynamic states of a DFIG are easily measurable. Furthermore, the proposed state estimation technique is decentralized and the network topology of the entire power system is taken into consideration in the estimation process. In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2015.2507620