Online parameter identification of synchronous machines using Kalman
filter and recursive least squares
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.
Submitted to IEEE IECON 2019.