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Online parameter identification of synchronous machines using Kalman filter and recursive least squares

Alves, Erick Fernando; Nøland, Jonas Kristiansen; Marafioti, Giancarlo; Mathisen, Geir
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URI
http://hdl.handle.net/11250/2633589
Date
2019
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Original version
10.1109/IECON.2019.8926707
Abstract
This paper investigates and implements a procedure for parameter identification of salient pole synchronous machines that is based on previous knowledge about the equipment and can be used for condition monitoring, online assessment of the electrical power grid, and adaptive control. It uses a Kalman filter to handle noise and correct deviations in measurements caused by uncertainty of instruments or effects not included in the model. Then it applies a recursive least squares algorithm to identify parameters from the synchronous machine model. Despite being affected by saturation effects, the proposed procedure estimates 8 out of 13 parameters from the machine model with minor deviations from data sheet values and is largely insensitive to noise and load conditions.
 
Online parameter identification of synchronous machines using Kalman filter and recursive least squares
 
Publisher
Institute of Electrical and Electronics Engineers (IEEE)

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