Open AccessIdentification of a synchronous generator parameters using recursive least squares and Kalman filter
Author(s) -
Diego Bravo,
Carlos Felipe Rengifo Rodas,
Cristian G. Giron,
Jhon Palechor
Publication year - 2021
Publication title -
ciencia en desarrollo
Language(s) - English
Resource type - Journals
eISSN - 2462-7658
pISSN - 0121-7488
DOI - 10.19053/01217488.v12.n1.2021.11779
Subject(s) - kalman filter , recursive least squares filter , control theory (sociology) , fast kalman filter , least squares function approximation , extended kalman filter , computer science , invariant extended kalman filter , generator (circuit theory) , filter (signal processing) , mathematics , algorithm , adaptive filter , statistics , artificial intelligence , control (management) , estimator , computer vision , power (physics) , physics , quantum mechanics
The comparison between recursive least squares (RLS) and Kalman filter (KF) is presented in this paper, both methods were adequate to estimate six parameters of a synchronous machine. The work focused on finding the operating conditions which the quality of the identification achieved with Kalman filter is better than recursive least squares. A linear model of the machine is used in order to considerate the currents and their derivatives as the system inputs while the three-phase voltage signals are the outputs. Furthermore two experiments with simulated and measured data were carried out, three operating scenarios and two variations of the algorithms respectively were considered. Despite the great similarity and good performance of both methods, it was found that Kalman filter slightly exceeded least squares due to the fact that it presented smaller oscillations in the estimated value of the parameters for any operating condition.