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Download fileOnline parameter identification of synchronous machines using Kalman filter and recursive least squares
preprint
posted on 2019-10-21, 23:01 authored by Erick AlvesErick Alves, Jonas Noeland, Giancarlo Marafioti, Geir MathisenThis 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
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.
Submitted to IEEE IECON 2019.
History
Email Address of Submitting Author
erick.f.alves@ntnu.noORCID of Submitting Author
0000-0002-7827-0380Submitting Author's Institution
NTNUSubmitting Author's Country
- Norway
