By Topic

Online State Estimation of a Synchronous Generator Using Unscented Kalman Filter From Phasor Measurements Units

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Esmaeil Ghahremani ; Department of Electrical and Computer Engineering, Laval University, Québec, Canada ; Innocent Kamwa

The most important reference quantities for monitoring and controlling transient stability in real time are the rotor angle and speed of the synchronous generators. If these quantities can be estimated with sufficient accuracy, they can be used in global and local control methods. In the classic state estimation methods, such as the extended Kalman filter (EKF) technique, the linear approximations of the system at a given moment in time may introduce errors in the states. In order to overcome the drawbacks of the EKF, the authors of this paper have applied the unscented Kalman filter (UKF) to estimating and predicting the states of a synchronous machine, including rotor angle and rotor speed, using phasor measurement unit (PMU) quantities. The UKF algorithm propagates the pdf of a random variable in a simple and effective way and is accurate up to the second order in estimating the mean and covariance. The overall impression is that the performance of the UKF is better than the EKF in terms of robustness, speed of convergence, and also different levels of noise. Simulation results including saturation effects and grid faults show the accuracy and efficiency of the UKF method in state estimation of the system, especially at higher noise ratios.

Published in:

IEEE Transactions on Energy Conversion  (Volume:26 ,  Issue: 4 )