Estimation of speed, stator temperature and rotor temperature in cage induction motor drive using the extended Kalman filter algorithm

The system parameters of induction motor (IM) vary significantly with different operating conditions. The temperature-dependent variation of the stator and rotor resistances, which induces a large estimation error on speed and flux, has been a critical issue for speed-sensorless control. To estimate simultaneous variations of the stator and rotor resistances in speed sensorless control of IMs under different operating conditions, an estimator is developed based on extended Kalman filter (EKF) technique. In order to resolve the instability issue of simultaneous estimation of stator and rotor resistances, the stator resistance is estimated via consideration of its temperature dependence and the thermal dynamics of the stator windings, whereas the rotor resistance is estimated as a constant state. Simulation results show that the EKF estimator, along with a controller developed based on the direct field-oriented control (DFOC) technique, achieves simultaneous estimation of the continuously changing resistances and maximum speed estimation error and speed control error of about 0.67%. The broken-bar phenomenon of rotor is also detected based on the rotor resistance estimation. Simulation results also display promising robustness to variation of system parameters, such as rotor and stator thermal time constants, which are not estimated by the proposed EKF algorithm.

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“…Considering the power losses and heat transfer to the ambient, a simple thermal model for the stator windings is derived based on [13]:…”

Section: Extended Model Of the Imsmentioning

confidence: 99%

EKF-based rotor and stator resistance estimation in speed sensorless control of induction motors

Chiang

Wang

Cheng

2012

2012 American Control Conference (ACC)

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“…A recent effort in research on sensorless drive control of standard induction motors has been the estimation of rotor speed from the measurement of stator voltages and currents using either the MRAS [4], [8] or the EKF [6], [7] and [9]. This work deals with a new approach for real-time estimation of mechanical speed and rotor flux components by using a reduced-order EKF.…”

Section: Introductionmentioning

confidence: 99%

“…This work deals with a new approach for real-time estimation of mechanical speed and rotor flux components by using a reduced-order EKF. Rather than a full-order EKF for speed estimation that also estimates stator current components, besides the estimation of rotor flux components [6], [7], rotor current components [9] or magnetizing current [10], an EKF algorithm based on a reduced-order statespace model is proposed in this paper. In this case only the rotor flux dq components are estimated, besides the rotor speed itself.…”

Section: Introductionmentioning

confidence: 99%

A new approach for speed estimation in induction motor drives based on a reduced-order extended Kalman filter

Leite

Araújo

Freitas

2004

2004 IEEE International Symposium on Industrial Electronics

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Abstract-This paper presents and proposes a new approach to achieve robust speed estimation in induction motor sensorless control. The estimation method is based on a reduced-order Extended Kalman Filter (EKF), instead of a fullorder EKF. The EKF algorithm uses a reduced-order statespace model structure that is discretized in a particular and innovative way proposed in this paper. With this model structure, only the rotor flux components are estimated, besides the rotor speed itself. Important practical aspects and new improvements are introduced that enable us to reduce the execution time of the algorithm without difficulties related to the tuning of covariance matrices, since the number of elements to be adjusted is reduced.

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“…Firstly, a new approach for real-time estimation of mechanical speed and rotor flux components using a reduced order EKF is presented and secondly, this approach is compared with the full order EKF that also estimates stator current components, besides the estimation of the speed itself and rotor flux components like in [6] and [7]. Instead of these flux components, the rotor current ones are estimated in [9] and the magnetizing current is estimated in [10].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Full and reduced order extended kalman filter for speed estimation in induction motor drives: a comparative study

Leite

Araújo

Freitas

2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551)

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Abstract-This paper presents a comparative study between a new approach for robust speed estimation in induction motor sensorless control, using a reduced order Extended Kalman Filter (EKF), and the one performed by the full order EKF. The new EKF algorithm uses a reduced order state-space model that is discretized in a particular and innovative way. In this case only the rotor flux components are estimated, besides the rotor speed, while the full order EKF also estimates stator current components. This new approach strongly reduces the execution time and simplifies the tuning of covariance matrices. The performance of speed estimation using both EKF techniques is compared with respect to computation effort, tuning of the algorithms, speed range including low speeds, load torque conditions and robustness relatively to motor parameter sensitivity.I.

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Estimation of speed, stator temperature and rotor temperature in cage induction motor drive using the extended Kalman filter algorithm

Cited by 44 publications

References 6 publications

EKF-based rotor and stator resistance estimation in speed sensorless control of induction motors

EKF-based rotor and stator resistance estimation in speed sensorless control of induction motors

A new approach for speed estimation in induction motor drives based on a reduced-order extended Kalman filter

Full and reduced order extended kalman filter for speed estimation in induction motor drives: a comparative study

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