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

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

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Sammendrag
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.
Språk
Engelsk
Forfatter(e)
Institusjon(er)
  • Norges teknisk-naturvitenskapelige universitet
  • SINTEF Digital / Mathematics and Cybernetics
  • Norges teknisk-naturvitenskapelige universitet
År
2019
Publisert i
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
ISSN
2577-1647
Forlag
IEEE conference proceedings
Bok
Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019
Hefte nr.
-
ISBN
978-1-7281-4878-6
Side(r)
7121 - 7128