• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.193-195
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• 2003

This paper presents an approach to estimate the power transmission line parameter by phasor measurement units(PMUs), which are synchronized to 1 pps signal of GPS. Existing approaches to estimate power transmission line parameter, are mainly off-line ones, based on faults or switching events on other neighboring lines. In this paper, to obtain static and dynamic properties of power transmission line parameter in service, the prototype of pmu-based Transmission Line Parameter Monitoring System (TLPMS) is proposed. Also, an technique to estimate parameters of transmission line described as 2-port network model and the soundness of estimated parameters are addressed.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.563-565
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• 2005

In this study, a algorithm to estimate Equivalent earth resistivity and Earth parameter using Artificial Neural Network(ANN) was proposed. Structures of the soil are grouped by using SOM algorithm before estimation. Earth parameter and Equivalent earth resistivity are obtained by using BP algorithm. The effectiveness of the proposed algorithm was verified. In the case study. afterwards, the algorithm proposed in this study will be used in more applications and gained more reliability.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.388-390
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• 1995

A new control scheme based on dead beat control with adaptive parameter estimation for PWM Inverter is proposed. The proposed scheme updates dead beat control parameters continouously, and make PWM inverter excellent performance at any load or parameter condition. Simulation results show very attractive features in this proposed scheme.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.9-11
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• 1998

Most of sensorless algorithms are based on motor equations including electrical and mechanical parameters. However, parameter variation and uncertain error decrease the accuracy of speed estimation of PMSM. This paper investigates the sensorless speed control of PMSM considering parameter variation. The proposed algorithm use the speed compensator which is robust in parameter variation and error. The simulation and experimental results indicate good performances.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.270-277
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• 1999

This paper deals with the maneuver detection and target tracking problem in uncertain parameter systems using a robust{{{{ { H}_{ } }}}} FIR filter to improve the unacceptable tracking performance due to the parametr uncertainty. The tracking filter used in the current paper is based on the robust{{{{ { H}_{ } }}}} FIR filter proposed by Kwon et al. [1,2] to estimate the state signal in uncertain systems with parameter uncertainty, and the basic scheme of the proposed method is the input estimation approach. Tracking performance of the maneuver detection and target tracking method proposed is compared with other techniques, Bogler allgorithm [4] and FIR tracking filter [2], via some simulations to examplify the good tracking performance of the proposed method over other techniques.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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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 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.38-46
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• 2015

This paper propose a variable parameter estimations for variable over current load of five-phase squirrel-cage induction motor(IM) to servo control system. In order to high performance control of AC motor using a field oriented control(FOC) and direct torque control(DTC) algorithm, there are required precise motor parameters for slip calculation, flux observer, controller gain, torque command of current components, rotor position, speed estimation, and so on. We are suggest a analyzed estimation results of the motor parameters that developing five-phase squirrel-cage IM have a stator of concentrated winding for experimental within variable over current load at rated input frequency. There are results of stator winding measurement, no-load test, locked-rotor test, variable over current load test, and estimated parameters of equivalent circuits using manufactured experimental apparatus by IEEE Standard Test Procedure for Polyphase Induction Motors and Generators 112-2004.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.75-76
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• 2016

The relationship of widely used Open circuit Voltage (OCV) versus State of Charge (SOC) is critical for any reliable SOC estimation technique. However, the hysteresis existing in all type of battery which has been come to the market leads this relationship to a complicated one, especially in Lithium Iron Phosphate (LiFePO4) battery. An accurate model for hysteresis phenomenon is essential for a reliable SOC identification. This paper aims to investigate and propose a method for hysteresis modeling. The SOC estimation is done by using Extended Kalman Filter (EKF), the parameter of the battery is modeled by Auto Regressive Exogenous (ARX) and estimated by using Recursive Least Square (RLS) filter to tract each element of the parameter of the model.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.326-330
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• 2001

A nonlinear speed control for a permanent magnet (PM) synchronous motor using a simple disturbance estimation technique is presented. By using a feedback linearization, scheme, the nonlinear motor model can be linearized. To compensate an undesirable output performance under the mismatch of the system parameters and load conditions the controller parameters will be estimated by using a disturbance observer theory. Since only the two reduced-order observers are used for the parameter estimation, the observer designs are considerably simple and the computational load of the controller for parameter estimation is negligibly small. The proposed control scheme is implemented on a PM synchronous motor using DSP TMS320C31 and the effectiveness is verified through the comparative experiments.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1212-1220
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• 2015

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.