• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.6
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• pp.299-306
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• 2000

The primary cause of air pollution by vehicles is imperfect combustion of fuel. One of the most usual causes of this imperfect combustion is the misfire in IC(Intenal Combustion) engine. Recently it is obligated for an ECU to monitor the emission level and warn the driver in case of exceeding specified emission standards. Therefore, in order to comply with this OBD-II regulations, car makers are investing a considerable amount into technology which would enable the detection of misfire and the particular cylinder in which misfire is taking place. So far, it has been able to detect misfire using engine speed, which can be obtained crank angle. However, such a method posed a problem in analyzing at high speed and in recognizing the misfire from the load impact at bumpy road. In this paper, misfire detection is made possible by simple arithmetic using WDFT, especially at high engine speed. In addition, the moving window method of a Walsh function is applied to determine the cylinders under misfire in case of multiple misfires. An actual experiment was conducted to prove that WDFT is applicable to effective in computation speed and to same result in misfire detection and cylinder determination at idle, part load and bumpy road conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.976-983
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• 2002

We presents a modified variable dimension filter with input estimation for maneuvering target tracking. The conventional variable dimension filter with input estimation(VDIE) consists of the input estimation(IE) technique and the variable dimension(VD) filter. In the VDIE, the IE technique is used for estimation of a maneuver onset time and its magnitude in the least square sense. The detection of the maneuver is declared according to the estimated magnitude of the maneuver. The VD filter structure is applied for the adaptation to the maneuver of the target after compensating the filter parameter with respect to the estimated maneuver when the detection of the maneuver is declared. The VDIE is known as one of the best maneuvering target tracking filter based on a single filter. However, it requires too much computational burden since the IE technique is performed at every sampling instance and thus it is computationally inefficient. We propose another variable dimension filter with input estimation named 'Modified VDIE' which combines VD filter with If technique. Modified VDIE has less computational load than the original one by separating maneuver detection and input estimation. Simulation results show that the proposed VDIE is more efficient and outperforms in terms of computational load.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.995-1000
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• 2017

In this paper, we present an electrical arc detection algorithm using the mash-up analysis technique which is the core technology for the autonomous electrical safety management system(AESMS) of the multi-unit dwellings. The mash-up analysis technique analyzes the voltage, load current, zero phase current data simultaneously to judge arc faults. In order to develop the arc fault detection algorithm, the characteristics of series arc and parallel arc were analyzed. Also, we propose the mash-up analysis technique that analyzes waveforms of voltage, load current, and zero phase current at the same time. The arc fault detection algorithm was developed using the mash-up analysis technique. The developed algorithm can prevent electrical disasters in an effective way through accident prediction, and it will be used as a basic technology to introduce an autonomous electrical safety management system.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.331-336
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• 2008

Electric load components have different characteristics according to the variation of voltage and frequency. This paper presents the load modeling of an electric locomotive by the parameter identification method. The proposed method for load modeling is very simple and easy for application. The proposed load model of the electric locomotive is represented by the combination of the loads that have static and dynamic characteristics. This load modeling is applied to the KTX in Korea to verify the effectiveness of the proposed method. The results of proposed load modeling by the parameter identification follow the field measurements very exactly.

• Journal of Power Electronics
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• v.14 no.6
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• pp.1303-1313
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• 2014

Dynamic behavior of the harmonic detection part of an active power filter (APF) has an essential role in filter compensation performances during transient conditions. Instantaneous power (p-q) theory is extensively used to design harmonic detectors for active filters. Large overshoot of p-q theory method deteriorates filter response at a large and rapid load change. In this study the harmonic estimation of an APF during transient conditions for balanced three-phase nonlinear loads is conducted. A novel fuzzy instantaneous power (FIP) theory is proposed to improve conventional p-q theory dynamic performances during transient conditions to adapt automatically to any random and rapid nonlinear load change. Adding fuzzy rules in p-q theory improves the decomposition of the alternating current components of active and reactive power signals and develops correct reference during rapid and random current variation. Modifying p-q theory internal high-pass filter performance using fuzzy rules without any drawback is a prospect. In the simulated system using MATLAB/SIMULINK, the shunt active filter is connected to a rapidly time-varying nonlinear load. The harmonic detection parts of the shunt active filter are developed for FIP theory-based and p-q theory-based algorithms. The harmonic detector hardware is also developed using the TMS320F28335 digital signal processor and connected to a laboratory nonlinear load. The software is developed for FIP theory-based and p-q theory-based algorithms. The simulation and experimental tests results verify the ability of the new technique in harmonic detection of rapid changing nonlinear loads.

• Journal of IKEEE
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• v.26 no.2
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• pp.313-318
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• 2022

The feedback voltage detection structure is proposed to alleviate overshoot and undershoot caused by the removal of the existing external output capacitor. Conventional LDO regulators suffer from overshoot and undershoot caused by imbalances in the power supply voltage. Therefore, the proposed LDO is designed to have a more improved transient response to form a new control path while maintaining only the feedback path of the conventional LDO regulator. A new control path detects overshoot and undershoot events in the output stage. Accordingly, the operation speed of the pass element is improved by charging and discharging the current of the gate node of the pass element. LDO regulators with feedback voltage sensing architecture operate over an input voltage range of 3.3V to 4.5V and have a load current of up to 200mA at an output voltage of 3V. According to the simulation result, when the load current is 200mA, it is 73mV under the undershoot condition and 61mV under the overshoot condition.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.2
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• pp.103-110
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• 2005

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.2
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• pp.199-210
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• 2003

In this paper, the new Islanding detection method is studied for utility interactive photovoltaic system(UIPVS). It describes the brief of UIPV system and the features of islanding phenomenon. The new islanding detection method for improving the detection characteristics, HCIM(Harmonic Current Injection Method), is proposed and analyzed. The impedance curve of AC load is derived from the complex power equation for testing Islanding detection features. The proposed detection method and the derivation of islanding condition we verified by the simulation with ACSL and the laboratorial experiments.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.29-31
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• 2005

Detection methods for atrial tachycardia and fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.