A review of RFO induction motor parameter estimation techniques

Toliyat, H.A.; Levi, E.; Raina, M.

doi:10.1109/tec.2003.811719

Cited by 334 publications

(54 citation statements)

References 150 publications

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“…The values of the model invariants can be determined by solving the equations of the real and imaginary parts of the equivalent impedance at a number of measurement points equal to half the number of invariants, although additional points are useful to filter deviations. An extensive review on parameter estimation for control applications, based on 207 references, is carried out on [18]. Many of the methods described employ the drive systems to perform tests or impose special excitations during the system startup.…”

Section: B Chronological Overviewmentioning

confidence: 99%

Estimation of Three-Phase Induction Motor Equivalent Circuit Parameters from Manufacturer Catalog Data

Wengerkievicz¹,

Elias²,

Batistela³

et al. 2017

J. Microw. Optoelectron. Electromagn. Appl.

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Section: B Chronological Overviewmentioning

confidence: 99%

Estimation of Three-Phase Induction Motor Equivalent Circuit Parameters from Manufacturer Catalog Data

Wengerkievicz¹,

Elias²,

Batistela³

et al. 2017

J. Microw. Optoelectron. Electromagn. Appl.

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“…If the control parameters do not match the actual values, the DFIG might not work properly: the closedloop system may be deteriorated or even become unstable. The electrical parameters of DFIG are sensitive to temperature changes, magnetic saturation and eddy currents [6]. Therefore, in most cases, the use of constant parameters does not allow to display the real dynamical behavior of the DFIG accurately.…”

Section: Introductionmentioning

confidence: 99%

“…Although there are several parameter estimation techniques available for induction machines [6], only few results are published on the parameter estimation of DFIG in variable-speed wind turbine systems [7]- [11]. In [7], two online parameter identification methods for DFIGs are proposed based on model reference adaptive systems (MRAS).…”

Section: Introductionmentioning

confidence: 99%

Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems

Abdelrahem¹,

Hackl²,

Kennel³

2015

2015 International Conference on Clean Electrical Power (ICCEP)

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This paper proposes a parameter estimation method for doubly-fed induction generators (DFIGs) in variable-speed wind turbine systems (WTS). The proposed method employs an extended Kalman filter (EKF) for estimation of all electrical parameters of the DFIG, i.e., the stator and rotor resistances, the leakage inductances of stator and rotor, and the mutual inductance. The nonlinear state space model of the DFIG is derived and the design procedure of the EKF is described. The observability matrix of the linearized DFIG model is computed and the observability is checked online for different operation conditions. The estimation performance of the EKF is illustrated by simulation results. The estimated parameters are plotted against their actual values. The estimation performance of the EKF is also tested under variations of the DFIG parameters to investigate the estimation accuracy for changing parameters.

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“…Therefore major efforts were put in for online estimation of rotor resistance. The online parameter estimation technique can be broadly classified as spectral analysis technique, observer based technique, model reference adaptive system technique and artificial intelligence techniques [7][8][9][10].…”

Section: Iintroductionmentioning

confidence: 99%

A novel on line rotor resistance estimation technique using EA tuned fuzzy controller for vector controlled induction motor drive

Chacko

Bhende

Jain

et al. 2015

2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)

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Induction motor with indirect field oriented control is well suited for high performance applications due to its excellent dynamic behavior. But, the indirect field oriented controller is sensitive to variations in rotor time constant, especially variation in rotor resistance. In this paper a scheme based on the Rotor flux Model Reference Adaptive Controller is used for on line identification of the rotor resistance and thus improving the steady state performance of the drive. The estimation of the actual rotor flux depends on the motor stator resistance, however its compensation is easier and the overriding feature of this estimation technique is the accurate identification of rotor resistance both during transient and steady state conditions when the drive is operating at full load and at zero motor speed condition. The effectiveness of the optimal designed Mamdani fuzzy controller based rotor identification scheme for four quadrant operation of motor drive is investigated and compared with the conventional trial and error based fuzzy controller in MA TLAB/Simulink environment.

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A review of RFO induction motor parameter estimation techniques

Cited by 334 publications

References 150 publications

Estimation of Three-Phase Induction Motor Equivalent Circuit Parameters from Manufacturer Catalog Data

Estimation of Three-Phase Induction Motor Equivalent Circuit Parameters from Manufacturer Catalog Data

Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems

A novel on line rotor resistance estimation technique using EA tuned fuzzy controller for vector controlled induction motor drive

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