• Journal of Power Electronics
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• v.15 no.6
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• pp.1567-1576
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• 2015

Offline inductance identification of a permanent magnet synchronous motor (PMSM) is essential for the design of the closed-loop controller and position observer in sensorless vector controlled drives. On the base of the offline inductance identification method combining direct current (DC) offset and high frequency (HF) voltage injection which is fulfilled at standstill, this paper investigates the inverter nonlinearity effects on the inductance identification while considering harmonics in the induced HF current. The negative effects on d-q axis inductance identifications using HF signal injection are analyzed after self-learning of the inverter nonlinearity characteristics. Then, both the voltage error and the harmonic current can be described. In addition, different cases of voltage error distribution with different injection conditions are classified. The effects of inverter nonlinearities on the offline inductance identification using HF injection are validated on a 2.2 kW interior PMSM drive.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1545-1552
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• 2017

Identifying the load and mutual inductance is essential for improving the power transfer capability and power transfer efficiency of Inductive Power Transfer (IPT) systems. In this paper, a steady-state load and mutual inductance identification method focusing on series-parallel compensated IPT systems is proposed. The identification model is established according to the steady-state characteristics of the system. Furthermore, two sets of identification results are obtained, and then they are analyzed in detail to eliminate the untrue one. In addition, the identification method can be achieved without extra circuits so that it does not increase the complexity of the system or the control difficulty. Finally, the feasibility of the proposed method has been verified by simulation and experimental results.

• 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.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.199-202
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• 2000

The accurate identification of the motor parameters in crucially important to achieve high dynamic performance of induction motors. In this paper parameter auto-tuning algorithms for stator(rotor) resistance stator(rotor) leakage inductance mutual inductance and rotor inertia. Stator(rotor) resistance and stator(rotor) leakage inductance are identified based on the stationary coordinate and mutual inductance and rotor resistor on the scalar speed control and the transient motor terminal voltage. To demonstrate the practical significance of our results we present some experimented results.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.3
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• pp.282-290
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• 2001

The accurate identification of the motor parameters is crucially important to achieve high dynamic performance of induction motors. In this paper, th motor parameters such as stator(rotor) resistance, stator(rotor) leakage inductance, mutual inductance, and rotor inertia are measured in off-line. Stator(rotor) resistance and stator(rotor) leakage inductance are measured based on the stationary coordinate equations of induction motors. On the other hand, mutual inductance are measured under the scalar control. Finally, the inverse rotor time constant is identified in on-line using an extended kalman filter algorithm. To demonstrate the practical significance of the results, Some experimental results are presented.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.786-787
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• 2006

The magnetic inductance of nanocrystalline $Fe_{73}Si_{16}B_7Nb_3Cu_1$ and an amorphous FeSiB powder sheet has been investigated to identify RFID performance. The powder was mixed with binder and solvent and tape-casted to form films. Results show annealing significantly influenced on the inductance of the material. The surface oxidation of the particles was the main reason for the reduced inductance. The maximum inductance of $Fe_{73}Si_{16}B_7Nb_3Cu_1$ alloy was about $88{\mu}H$ at 17.4 MHz, about 65% greater compared to the FeSiB alloy. The higher inductance in the nanocrystalline alloy indicates it may be used as a potential replacement of current RFID materials.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.3
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• pp.288-294
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• 2013

This paper introduces a novel method of on-line identifying the stator resistance and leakage inductance of dual three phase induction motor (DTPIM). According to the machine mathematical model, the stator resistance and leakage inductance can be estimated using the voltage and current values in harmonic subspace. Thus a method of voltage vector injection in harmonic subspace (VVIHS) is proposed, which causes currents in harmonic space. Then the errors between command and actual harmonic currents are utilized to regulate the machine parameters, including stator resistance and leakage inductance. The principle is presented and analyzed in detail. Experimental results prove the feasibility and validity of proposed method.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.723-725
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• 2000

This paper presents a parameter identification method to estimate the stator resistance. stator inductance, rotor resistance and rotor inductance of the induction motor. A step voltage is applied across the stator terminals while the machine is in the standstill condition, and the resulting stator voltage and current response are measured. Observer/Kalman Filter Identification(OKID) algorithm is used to estimate induction motor parameters. Simulation results are presented to verify the identified model.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.131-134
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• 2001

The accurate identification of the motor parameters is crucially important to achieve high dynamic performance of induction motors. In this paper, the motor parameters such as rotor resistance, stator(rotor) leakage inductance, mutual inductance are measured for torque monitoring and indirect vector control. To demonstrate the practical significance of the results, some experimental results are presented.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.110-116
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• 2017

This paper proposed a new real-time parameter tracking algorithm. Unlike the convenience algorithms, the proposed real-time parameter tracking algorithm can estimate parameters through three-phase voltage and electric current without coordination transformation, and does not need information on magnetic flux. Therefore, it can estimate parameters regardless of the change according to operation point and cross-saturation effect. In addition, as the quasi-real-time parameter tracking technique can estimate parameters through the four fundamental arithmetic operations instead of complicated algorithms such as numerical value analysis technique and observer design, it can be applied to low-performance DSP. In this paper, a new real-time parameter tracking algorithm is derived from three phase equation. The validity and usefulness of the proposed inductance estimation technique is verified by simulation and experimental results.