A new current model flux observer for wide speed range sensorless control of an induction machine

Rehman, Habibur; Derdiyok, Adnan; Guven, M.K.; Xu, Longya

doi:10.1109/tpel.2002.805579

Cited by 96 publications

(30 citation statements)

References 33 publications

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“…In the sampled time domain, (14) can be generalized to an arbitrary time kT as the final step, and the final recursive crosscoupled state equation for stator flux linkage is obtained as…”

Section: Low-sf Flux Observermentioning

confidence: 99%

A Low-Switching-Frequency Flux Observer and Torque Model of Deadbeat–Direct Torque and Flux Control on Induction Machine Drives

Wang

Tobayashi

Lorenz

2015

IEEE Trans. on Ind. Applicat.

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This paper presents a digital implementation of deadbeat-direct torque and flux control (DB-DTFC) for induction machines at low switching frequencies (SFs). For high SFs, existing discrete-time flux observers and Volt-second-based inverse torque models used for DB-DTFC achieve acceptable flux estimation accuracy and fast torque response. However, the flux estimate is less accurate when the SF is reduced and the DB-DTFC performance degrades. This paper develops a more suitable flux observer and Volt-second-based inverse torque model that minimizes flux estimation error and improves torque control for DB-DTFC. Simulation and experimental results are provided to evaluate the performance of the proposed observer and torque model at very low SFs. Consequently, digital implementation of low-SF DB-DTFC on high-power induction machines is feasible. Index Terms-Deadbeat-direct torque and flux control (DB-DTFC), flux observer, low-switching-frequency (SF) operation.NOMENCLATURE ( ) s Stationary reference frame. ( ) r Rotor reference frame. ( ∧ ) Estimation quantity. ( ) * Reference quantity. ( · ) Time derivative operator. ( ) s Quantity on the stator side. ( ) r Quantity on the rotor side. ( ) qdx Complex vector quantity. (k) Quantity at the kth sampling time. (s) Quantity in the Laplace domain. s Laplace operator. v qds Stator voltage complex vector. i qds Stator current complex vector. i qdr Rotor current complex vector. λ qds Stator flux linkage complex vector. i qdr Rotor flux linkage complex vector. R s Stator resistance. R r Rotor resistance. L m Magnetizing inductance. L ls Stator leakage inductance. L lr Rotor leakage inductance. σ = 1 − (L m L m /L s L r ) Total leakage factor. τ r = R r /L r Rotor time constant. ω r Electrical angular velocity of rotor. ω br = (R r /L r ) − jω r Rotor break frequency. T e Electromagnetic torque. T s Sampling time.

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“…In the sampled time domain, (14) can be generalized to an arbitrary time kT as the final step, and the final recursive crosscoupled state equation for stator flux linkage is obtained as…”

Section: Low-sf Flux Observermentioning

confidence: 99%

A Low-Switching-Frequency Flux Observer and Torque Model of Deadbeat–Direct Torque and Flux Control on Induction Machine Drives

Wang

Tobayashi

Lorenz

2015

IEEE Trans. on Ind. Applicat.

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“…Equations (16) and (17) are speed independent and form the reference model, whereas (18) and (19) equations depend on a flux calculation on the speed and therefore can be used as an adaptive model.…”

Section: Rotor Flux Mras Estimatormentioning

confidence: 99%

“…In the literature, many sensorless strategies have been introduced, and most of them can be categorized in two main categories, model-based sensorless strategies [11][12][13][14], which use the machine basic model to estimate the speed of the rotor, and spectral analysis strategies which use the rotor positiondependence feature which exists in many AC motors to determine the rotor speed or position [12,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Predictive sensorless control of induction motor drives

Zbede

Gadoue

Atkinson

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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This paper presents a control system of an Induction Machine (IM) with predictive controller-estimator. A novel speed estimator is proposed, and the estimator operation is examined at different operational conditions. The new estimator shows an improvement in the speed estimation during the transient operation compared to the conventional estimators, and it also shows a satisfactory operation at different operating conditions. Simulations results are presented and the proposed experimental rig is described.

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“…For that, select the positive Lyapunov function . After differentiation, and are replaced from (21). Then: (24), since Δ 0, there are two undesirable terms and cannot be made strictly negative definite.…”

Section: Framementioning

confidence: 99%

An MRAS-type estimator for the speed, flux magnitude and rotor flux angle of the induction motor using sliding mode

Comanescu

2014

2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion

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The paper discusses the problem of estimating the speed, the flux magnitude and the rotor flux angle of the induction motor (IM) and presents an estimation method based on two Sliding Mode Observers (SMOs) and the Model Reference Adaptive System (MRAS) technique. The method is based on implementation of two SMOs that both yield the magnitude of the rotor flux; one observer is the reference model, the other is the adjustable model. The MRAS method is used to adapt the speed signal which is an input into both SMOs. The reference model is designed using the equations of the IM in the rotating reference frame. It is shown that its estimated flux magnitude is insensitive to the input speed. The adjustable model uses the IM equations in the stationary reference frame. Its output fluxes have magnitudes inverse proportional with the input speed; however, their phases are always accurate (this allows estimation of the flux angle). Using MRAS, the speed is corrected such that the flux magnitudes coming out of the two models match. Based on the structure developed, the paper also a speed estimation method. The simulations validate the theoretical development.

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A new current model flux observer for wide speed range sensorless control of an induction machine

Cited by 96 publications

References 33 publications

A Low-Switching-Frequency Flux Observer and Torque Model of Deadbeat–Direct Torque and Flux Control on Induction Machine Drives

A Low-Switching-Frequency Flux Observer and Torque Model of Deadbeat–Direct Torque and Flux Control on Induction Machine Drives

Predictive sensorless control of induction motor drives

An MRAS-type estimator for the speed, flux magnitude and rotor flux angle of the induction motor using sliding mode

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