State estimation of induction motor using unscented Kalman filter

Date

2003-06-25

Author

Akin, B
Orguner, Umut
Ersak, A

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In this paper, a new estimation technique, unscented Kalman filter (UKF) is applied to state observation in field oriented control (FOC) of induction motor. UKF, a recent derivative-free nonlinear estimation tool, is used for estimating rotor speed and fluxes using sensed stator current and voltages. In the simulations, UKF, whose several intrinsic properties suggest its use over EKF in highly nonlinear systems, turned out to be very similar to EKF in flux estimates. The simulation results also show that UKF has slightly better speed estimation performance than EKF while driven under the identical machine model and parameters (covariances).

Subject Keywords

Induction motor, Speed estimation, UKF, EKF, Sensorless vector control, State observer

URI

https://hdl.handle.net/11511/52668

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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State estimation techniques for speed sensorless field oriented control of induction motors Akın, Bilal; Ersak, Aydın; Department of Electrical and Electronics Engineering (2003) This thesis presents different state estimation techniques for speed sensorlees field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations and experiments individually. First, a stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis rotor fluxes and dq-axis stator currents. Thus, using this observer the rotor spe...
An experimental performance test of a derivative-free non-linear state observer designed for sensorless AC drives Akın, B; Orguner, Umut; Ersak, Aydın (2004-06-05) In this paper, a new Kalman Filtering technique, Unscented Kalman Filter (UKF) is utilized both experimentally and theoretically as a state estimation tool in field-oriented control (FOC) of sensorless AC drives. Using the advantages of this recent derivative-free nonlinear estimation tool, rotor speed and dq-axis fluxes of an induction motor are estimated only with the sensed stator currents and voltages information. In a previous study, simulations has shown that, UKF, whose several intrinsic properties s...
A comparative study on Kalman filtering techniques designed for state estimation of industrial AC drive systems Akin, B; Orguner, Umut; Ersak, A (2004-06-05) In this paper, two different Kalman Filtering techniques, Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) are investigated and compared both experimentally and theoretically. These non-linear, stochastic observers are employed as a state estimation tool in field-oriented control (FOC) of sensorless AC drives in this work. Using the superiorities of Kalman filtering, rotor speed and dq-axis fluxes of an induction motor are estimated only with the sensed stator currents and voltages information...
Vector Magnetic Characteristic Analysis of Induction Motor considering Effect of Harmonic Component due to Secondary Slot Enokizono, Masato; Kunihiro, Naoki (2011-09-10) This paper presents load characteristics of a three-phase induction motor model core calculated with the finite element method considering two-dimensional vector magnetic properties. Influence of harmonic components due to the secondary slot of the rotor core on the total iron loss is investigated. In this paper we propose a new iron loss calculation method to consider higher harmonic components.
A Comparative Study on Non-Linear State Estimators Applied to Sensorless AC Drives: MRAS and Kalman Filter Akin, Bilal; Orguner, Umut; Ersak, Aydin; Ehsani, Mehrdad (2004-11-06) In this paper, two different nonlinear estimators applied to sensorless AC drives, Kalman Filtering techniques (EKF and UKF) and Model Reference Adaptive System (back emf ans reactive power models), are discussed and compared to each other. Both of the observer types are studied and analyzed both experimentally and theoretically. In order to compare the observers precisely, the observers are tested under the identical conditions.

Citation Formats

B. Akin, U. Orguner, and A. Ersak, “State estimation of induction motor using unscented Kalman filter,” 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52668.