Online parameter identification of synchronous machines using Kalman filter and recursive least squares
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.
Academic chapter/article/Conference paper
- Norwegian University of Science and Technology
- SINTEF Digital / Mathematics and Cybernetics
IEEE conference proceedings
Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019
7121 - 7128