• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.656-659
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• 2004

As the demand for traceable calibrations of torque measuring devices has considerably increased both in the production sector and in research institutes, suitable standard machines had to be developed at the Korea Research Institute of Standards and Science. Owing to its special design, the small uncertainty of measurement required for the realization of the static torque can be reached (relative uncertainty of measurement ＆lt; 5$\times$10$^{-5}$ in the measurement range between 500 and 2000 Nm, and ＆lt; 1$\times$10$^{-4}$ in the measurement range from 10 to 500 Nm). The relative discrepancy between our torque calibration results of 2 kNm and PTB s (Physikalisch Technische Bundesanstalt, Germany) results was less than 2$\times$10$^{-5}$ , which confirming our uncertainty estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.230-230
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• 2000

This paper describes a model reference adaptive system(MRAS) for speed control of vector-controlled induction motor without a speed sensor. The proposed approach is based on observing the instantaneous torque. The real torque is calculated by sensing stator current and estimated torque is calculated by stator current that is calculated by using estimated rotor speed. The speed estimation error is linearly proportional to error between real torque and estimated torque. The proposed feedback loop has linear component. Furthermore proposed method is robust to parameters variation. The effectiveness is verified by equation and simulation

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.9
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• pp.26-35
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• 2010

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. This controller is controlled speed and current using hybrid PI(HBPI) controller and estimation of speed using ANN. Also, this paper is proposed maximum torque control of induction motor using slip angular speed and current condition at widely speed range. The performance of the proposed induction motor drive with maximum torque control using HBPI controller is verified by analysis results at dynamic operation conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2001.11a
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• pp.181-185
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• 2001

This paper presents a digitally speed sensorless control system for induction motor with direct torque control (DTC). The drive is based on Mode1 Reference Adaptive System (MRAS) using state observer as a reference model fat flux estimation. The system are closed loop stator flux and torque observer for wide speed range that inputs are currents and voltages sensing of motor terminal, model reference adaptive control (MRAS) with rotor flux linkages for the speed turning aignal at low speed range, two hysteresis controllers. The Proposed system is verified through simulation.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.421-424
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• 2000

Direct Torque Control(DTC) of AC motor has the fast torque and flux dynamic responses even though it has very simple scheme to implement. However DTC do not show good performance at low speed range with conventional open loop stator flux observer when stator resistance varied. So authors proposed nonlinear stator flux obsever in order to flux estimation at low speed and show its simulation results.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1156-1165
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• 2018

This paper presents a detailed realization of direct torque controlled induction motor drive for elevator applications. The drive is controlled according to the well-known space vector modulated direct control scheme (SVM-DTC). As the elevator drives are usually equipped with speed sensors, flux estimation is carried out using a current model where two stator currents are measured and accurate instantaneous rotor speed measurement is used to overcome the need for measuring stator voltages. Speed profiling for a comfortable elevator ride and other supervisory control activities to provide smooth operation are also explained. The drive performance is examined and controllers' parameters are fine-tuned using MATLAB/SIMULINK. The blocks used for flux and torque estimation and control in the offline simulation are compiled for real-time using dSPACE Microlabox. The performance of the drive has been verified experimentally. The results show good performance under transient and steady state conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• Wind and Structures
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• v.21 no.4
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• pp.443-460
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• 2015

In this paper, an integrator based method to estimate the effective wind speed in wind turbine systems is proposed. First, the aerodynamic torque was accurately estimated through a proportional gain based observer where the generator speed is the measured output of the system. The torque signal contains not only useful frequencies of the wind, but also high frequencies and the ones due to structural vibration. The useful information of the wind signal is low frequency. A spectral analysis permitted the determination of the useful frequencies. The high frequencies were then filtered before introducing the torque signal in the wind speed observer. The desired effective wind speed was extracted through an integrator based observer using the previously estimated aerodynamic torque. The strength of the method is to avoid numerical solutions used in literature of the wind speed estimation. The effectiveness of the proposed wind speed estimator and its use to control the generator speed has been tested under turbulent situations using the FAST software (Fatigue, Aerodynamics, Structures, and Turbulence), for large scale Megawatt turbine.

• Journal of Fisheries and Marine Sciences Education
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• v.7 no.1
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• pp.127-132
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• 1995

In this paper, a new speed estimation method of induction motors based on the very quick torque control is proposed to realize speed sensorless control. The proposed method can be realized very simply by detecting primary motor current and voltage command at every sampling time. As the method need not the differential value of primary current in a arithmetic of voltage command, it can be expected to promote the precision of speed estimation in low speed area, especially. Through the numerical simulation, the validity of the proposed method was successfully confirmed.