A Study of Sensorless Control of Induction Motor at Zero Speed Utilizing High Frequency Voltage Injection

Drevensek, D.; ܞarko, Damir; Lipo, T.Α.

doi:10.1080/09398368.2003.11463534

Cited by 12 publications

(3 citation statements)

References 9 publications

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“… Model Reference Adaptive System (MRAS) [5], [6];  Extended Luenberger observers [7];  Extended Kalman filter (EKF) [8];  Instantaneous reactive power [4];  Artificial neural network (ANN) [9];  Harmonic caused by machine saliency [10];  Carrier frequency signal injection in the stator windings [10]. This paper introduces a vector control based on a PI model reference adaptive system for dual star motor sensorless drives.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Speed Sensorless Vector Control of Dual Star Induction Motor Drive Using MRAS Approach

Kouzi¹,

Seghier²,

Natouri³

2015

IJEEE

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This paper investigates a fuzzy speed sensorless vector control of Dual Star Induction Motor (DSIM) using MRAS approach. The rotor flux and speed estimator based on a PI model reference adaptive system (MRAS) is developed. The error in the instantaneous phase position of the two rotor flux estimates is utilized for rotor speed estimation. In order to achieve high performances in term of fast dynamic speed response and best disturbance rejection, fuzzy logic controller (FLC) is used for speed regulation. The proposed scheme is recommended for applications requiring robust speed control even in presence of some key parameters deviation. The performance of the suggested scheme has been tested through some simulation results.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Speed Sensorless Vector Control of Dual Star Induction Motor Drive Using MRAS Approach

Kouzi¹,

Seghier²,

Natouri³

2015

IJEEE

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“…Speed estimation methods are being used that avoid the speed measurement set-up and commercial sensorless vector-controlled drives are already available. There are several speed estimation techniques in the literature which are generally classified in [3] as follows: slip calculation; direct synthesis from state equations; model reference adaptive system (MRAS) [4]; speed adaptive flux observer; slot harmonics; injection of auxiliary signal on salient rotor to improve the estimation at low speed [5] and extended Kalman filter (EKF) based methods, [6], [7].…”

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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“…There are several speed estimation techniques in the literature which are generally classified in [3] as follows: slip calculation; direct synthesis from state equations; model reference adaptive system (MRAS) [4]; speed adaptive flux observer; slot harmonics; injection of auxiliary signal on salient rotor to improve the estimation at low speed [5] and extended Kalman filter (EKF) based methods, [6], [7].…”

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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A Study of Sensorless Control of Induction Motor at Zero Speed Utilizing High Frequency Voltage Injection

Cited by 12 publications

References 9 publications

Fuzzy Speed Sensorless Vector Control of Dual Star Induction Motor Drive Using MRAS Approach

Fuzzy Speed Sensorless Vector Control of Dual Star Induction Motor Drive Using MRAS Approach

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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