• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.2
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• pp.92-98
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• 2019

Accurate estimation of motor parameters is required in some motor control applications. For example, the value of stator resistance is required for stator flux-oriented control mostly used in doubly fed induction generator systems. Stator resistance is not a constant value and continuously changes due to the rise in temperature during motor operation. Estimation errors degrade the control performance. Hence, this study proposes a simple stator resistance estimation method. In this scheme, the differential components of voltage and current values are used to eliminate the dead-time effect, and Kalman filter algorithm is applied to reduce the error according to measurement noise. Simulation and experimental results obtained with a permanent magnet motor show the validity of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.550-558
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• 2014

Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

• Journal of Power Electronics
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• v.19 no.3
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• pp.751-760
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• 2019

Since parameter mismatch seriously impacts the efficiency and stability of induction motor drives, it is important to accurately estimate the rotor and stator resistance. This paper introduces a method to directly calculate the rotor flux that is independent of stator and rotor resistance and electrical angle. It is based on obtaining the rotor and stator resistance using the model reference adaptive system (MRAS) method. The method has a lower computation burden and less adaptation time when compared with other rotor resistance estimation methods. This paper builds three coordinate frames to analyze the rotor flux error and rotor resistance error. A number of implementation issues are also considered.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.253-263
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• 2002

This paper presents an adaptive feedback linearization control scheme for induction motors using stator fluxes. By using stator flukes as states, overparameterization is prevented and control inputs can be determined straightforwardly unlike in existing schemes. This approach leads to the decrease of the relative degree for the flux modulus and thus yields a simpler control algorithm than the prior results. In this paper. adaptation schemes are suggested to compensate for the variations of stator resistance. rotor resistance and load torque. In particular, the adaptation to the variation of stator resistance with a feedback linearization control is a new trial. In addition, to improve the convergence of rotor resistance estimation, the differences between stator currents and its estimates are used for the parameter adaptation. The simulations show that torque and flux are controlled independently and that the estimates of stator resistance, rotor resistance, and load torque converge to their true values. Actual experiments on a 3.7㎾ induction motor verify the effectiveness of the proposed method.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.742-747
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• 2006

This paper describes online stator-resistance estimation of a linear induction motor(LIM) with cage-type secondary using direct thrust control(DTC), where the resistance value is derived from stator-winding temperature estimation using thermocouple. In this paper, corrected stator resistance has an error in actuality measurement resistance. So compensation coefficient $\kappa$ which is decided through comparison and verifying several times relation of calculated resistance and measured motor line-line resistance. The stator-winding temperature information can also be used for monitoring, protection, and fault-tolerant control of the machine. Also, this paper reports the LIM's responses of the flux measured by the proposed stator resistance compensation algorithm.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.451-461
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• 2010

Electric vehicles (EVs) require fast torque response and high drive efficiency. This paper describes a control scheme of fuzzy direct torque control of permanent magnet synchronous motor for EVs. This control strategy is extensively used in EV application. With direct torque control (DTC), the electromagnetic torque and stator flux can be estimated using the measured stator voltages and currents. The estimation depends on motor parameters, except for the stator resistance. The variation of stator resistance due to changes in temperature or frequency downgrades the performance of DTC, which is controlled by introducing errors in the estimated flux linkage vector and the electromagnetic torque. Thus, compensation for the effect of stator resistance variation becomes necessary. This work proposes the estimation of the stator resistance and its compensation using a proportional-integral estimation method. An electronic differential has been also used, which has the advantage of replacing loose, heavy, and inefficient mechanical transmission and mechanical differential with a more efficient, light, and small electric motors that are directly coupled to the wheels through a single gear or an in-wheel motor.

• Journal of Power Electronics
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• v.13 no.6
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• pp.985-990
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• 2013

This paper proposes a novel fault diagnosis scheme using parameter estimation of the stator resistance, especially in the case of the open-phase faults of PMSM drives. The stator resistance of PMSMs can be estimated by the recursive least square (RLS) algorithm in real time. Fault diagnosis is achieved by analyzing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the estimated parameter information can be used to improve the control performance. The feasibility of the proposed fault diagnosis scheme is verified by simulation and experimental results.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.3
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• pp.289-295
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• 2013

It is important to know exact values of Interior Permanent Magnet Synchronous Motors(IPMSM)' parameters such as stator resistance and inductance in order to have their high performance. This paper proposes a novel motor parameter(stator resistance, d&q axis inductance) estimation algorithm for IPMSM. The proposed estimation method utilizes back-EMF equations and model reference adaptive system(MRAS). The algorithm using back-EMF estimates d and q axis inductances in the constant torque region, and the stator resistance is estimated by using MRAS with the estimated inductance regardless of speed regions. The validity of the proposed algorithm is verified by simulations and experiments.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.1
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• pp.37-46
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• 2004

A novel rotor speed estimation method using model reference adaptive control(MRAC) is proposed to improve the performance of a sensorless vector controller. In the proposed method, the stator current is used as the model variable for estimating the speed. In conventional MRAC methods, the relation between the two model errors and the speed estimation error is unclear. In the proposed method, the stator current error is represented as a function of the first degree for the error value in the speed estimation. Therefore, the proposed method can produce a fast speed estimation. The robustness of the rotor flux-based MRAC, back EMF-based MRAC, and proposed MRAC is compared based on a sensitivity function about each error of stator resistance, rotor time constant, mutual inductance. Consequently, the proposed method is much more robust than the conventional methods as regards errors in the mutual inductance, stator resistance. Therefore, the proposed method offers a considerable improvement in the performance of a sensorless vector controller at a low speed. In addition, the superiority of the proposed method and the validity of sensitivity functions were verified by simulation and experiment.

• Journal of Power Electronics
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• v.18 no.2
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• pp.492-501
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• 2018

In the rotor-flux oriented control used in induction motors, the electrical parameters of the motors should be identified. Among these parameters, the mutual inductance and rotor resistance should be accurately tuned for better operations. However, they are more difficult to identify than the stator resistance and stator transient inductance. The rotor resistance and mutual inductance can change in operations due to flux saturation and heat generation. When detuning of these parameters occurs, the performance of the control is degenerated. In this paper, a novel method for the concurrent identification of the two parameters is proposed based on recursive least square estimation and model reference adaptive control.