On-Line Estimation of Magnetizing Inductance and Rotor Resistance in Extended Kalman-Filter for Induction Machines
- Resource Type
- Conference
- Authors
- Gashtil, H.; Pickert, V.; Atkinson, D.; Giaouris, D.; Dahidah, M.
- Source
- 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) EECS Electrical Engineering and Computer Science (EECS),2018 2nd European Conference on. :582-588 Dec, 2018
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Estimation
Mathematical model
Rotors
Induction machines
Kalman filters
Covariance matrices
Stators
Extended Kalman filter, induction motors parameters, on line estimation, induction motor derives
- Language
This paper proposes an on-time estimation of induction machine parameters based on the extended Kalman-filter. So far, the real-time performance of Extended Kalman-filter algorithms has not been validated in the variation of motor parameters. Furthermore, the conventional parameter estimations in extended Kalman-filter has not been developed accurately based on the correct non-linear state space model. This paper proposes a new state space model of induction machine included motor parameters with less dependency to other model variables. This leads to achieve more accurate estimation results even in a situation where a sudden variation of motor parameters happens. This paper describes the proposed method analytically. Simulation results for an induction machine are presented and discussed.