Identification of a synchronous generator parameters using recursive least squares and Kalman filter

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 fi...

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书目详细资料
Main Authors: Bravo Montenegro, Diego Alberto, Rengifo, Carlos Felipe, Giron, Cristian, Palechor, Jhon
格式: Online
语言:spa
出版: Universidad Pedagógica y Tecnológica de Colombia 2021
主题:
在线阅读:https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/11779
实物特征
总结: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.