• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1110-1115
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• 2009

We studied on the design of electrical parameters for ICP(Inductively Coupled Plasma) light sources which can be effective to improve the electrical power efficiency of it. These parameters were derivated from Barkhausen theory about the oscillating condition of a ballaster. The relationships of $f-I_p$ and f-n were calculated theoretically and then these relationships were compared with the measured results about $I_p$ and power depending to a discharge length(l) of ICP light source. Finally, we can see that a specific range of induced current depending to a discharge length would be necessary to minimize the change of magnetization inductance and driving frequency at driving.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.324-329
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• 1999

This paper proposes new torque control of an induction motor, which is robust against time verying parameters. The control is based on adaptive vector control with serial block adaptive algorithm. Motor parameters used to estimates slip frequency and torque. Frequency mismatch in the control system detrimentally affects slip frequency estimation and torque response. In order to compensate for degradation of the responses an adaptive identifier for the magnetizing inductance and the secondary time constand is introduced. adaptive vector control system consisted of two subsystems, a vector control system realized on synchronous frame and a parameter identification system on stationary frame. the effectiveness of the proposed method was verified by some digital simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07b
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• pp.607-610
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• 2003

The characteristic prediction and analysis of 3-dimensional (3-D) solenoid-type embedded inductors is investigated. The four different structures of 3-D inductor are fabricated by using low-temperature cofired ceramic (LTCC) process. The circuit model parameters of the each building block are optimized and extracted using the partial element equivalent circuit method and HSPICE circuit simulator. Based on the model parameters, predictive modeling is applied for the structures composed of the combination of the modeled building blocks. And the characteristics of test structures, such as self-resonant frequency, inductance and Q-factor, are analyzed. This approach can provide the characteristic conception of 3-D solenoid embedded inductors for structural variations.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.47-48
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• 2005

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.6
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• pp.738-747
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• 1999

Vector controlled induction motor have been widely used in high performance applications. How-ever the performance is sensitive to the variations of motor parameters especially the rotor time constant which varies with the temperature and the saturation of the magnetizing inductance. In this paper the authors propose new identifying method for time-varying parameters of an induction motor which is based on adaptive vector control with serial block algorithm. Vector con-trol system realized on synchronous frame and parameter identification system realized on sta-tionary frame are not easily affected by the vector control frame. Parameter mismatch in the control system results in heavy transient variation in speed and torque response. In order to compensate degradation of the responses at the middle and low speed region adaptive identifier is introduced. To verify the feasibility of this technique compute simu-lations carried out.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1402-1408
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• 2010

Interior Permanent Magnet Synchronous Motor(IPMSM) produces two kind of torque that Magnetic and Reluctance torque. The permanent magnet linkage flux Ψa and d-axis and q-axis inductance have an important influence on the torque characteristic of IPMSM. Thus their accurate prediction is essential for predicting performance aspect such as the torque and flux-weakening capabilities. In this paper, we compared the characteristic parameters and operation region of two type IPMSM which has different pole numbers.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.731-732
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• 2008

This paper deals with design and characteristics analysis of 7.5-kW, 60,000-rpm class permanent magnet synchronous motor for turbo compressor. In order to determine the design parameters of rotor, torque per rotor volume method is applied. And, to analyze the magnetic field distribution and estimate the electrical parameters such as back EMF constant, inductance and torque constant, electromagnetic transfer relations theorem is employed. We compare the characteristics analysis results of model designed by proposed method with those by nonlinear FEA. As a result of this, the design have been validated.