Niño-Barón, Carlos E.

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    Dealing with ill conditioning in recursive parameter estimation for a synchronous generator
    (2006) Niño-Barón, Carlos E.; Vélez-Reyes, Miguel; College of Engineering; O’Neill-Carrillo, Efraín; Irrizary-Rivera, Agustín; Department of Electrical and Computer Engineering; Colucci-Ríos, José A
    This thesis presents how to deal with ill-conditioning in recursive parameter estimation for a synchronous generator using subset selection, the Extended Kalman Filter (EKF), and the Iterated Extended Kalman Filter (IEKF). We present how the quality of the estimates in ill-conditioned parameter estimation problems is significantly affected by noise and how by proper modifications to the EKF, we still extract useful parameter estimates from low quality data. The modifications to the EKF and IEKF are based on the subset selection method, where only a subset of parameters is estimated from the available data and the other parameters are fixed to prior values. The reduced order parameter estimation problem is better conditioned allowing the extraction of good estimates from the available data. Simulation studies on the identification of a linearized model of a synchronous generator are used to illustrate the concepts being studied in this work. Simulation results show how the modifications to the EKF and IEKF based on the subset selection method result in convergent algorithms when their application to the original full problem was not. We also show that for this case the additional computational effort needed for the IEKF does not result in significant improvement in the quality of the estimates over those obtained with EKF.