• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권6호
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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.

• 제어로봇시스템학회논문지
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• 제8권11호
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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.

• 전기학회논문지
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• 제66권6호
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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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• 제3권3호
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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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• 제14권6호
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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.

• 전기전자학회논문지
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• 제26권2호
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• pp.313-318
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• 2022

피드백 전압 감지 구조는 기존 외부 출력 캐패시터의 제거로 인한 오버슈트 및 언더슈트 현상을 완화하기 위해 제안된다. 기존의 LDO 레귤레이터는 전원 공급 전압의 불균형으로 인해 발생하는 오버슈트 및 언더슈트를 겪는다. 따라서 제안된 LDO는 기존 LDO의 피드백 경로만 유지하면서 새로운 제어 경로를 형성하기 위해 보다 개선된 과도 응답을 갖도록 설계되었다. 새로운 제어 경로는 출력 단계에서 발생하는 오버슈트 및 언더슈트 현상을 감지한다. 이에, 패스 소자의 게이트 노드의 전류를 충방전함으로써 패스 소자의 동작 속도가 향상된다. 피드백 전압 감지 구조가 있는 LDO 레귤레이터는 3.3~4.5V의 입력 전압 범위에서 작동하며 3V의 출력 전압에서 최대 200mA의 부하 전류를 가집니다. 시뮬레이션 결과에 따르면 부하전류가 200mA일 때 언더슈트 조건에서는 73mV, 오버슈트 조건에서는 61mV이다.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제5B권2호
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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.

• 전력전자학회논문지
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• 제8권2호
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• pp.199-210
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• 2003

본 논문에서는 계통연계형 태양광발전시스템의 고립운전방지를 위한 새로운 고리운전 검출기법을 연구한다. 이를 위하여 계통연계형 태양광발전시스템 및 고립운전시의 특성에 대하여 기술하였다. 또한 고립운전의 검출특성을 향상시키기 위한 새로운 검출기법을 제시하고 해석하였으며 검출시험을 위한 부하조건 선정을 위한 부하임피던스 곡선을 해석적으로 유포하였다 ACSL 시뮬레이션 및 실험을 통하여 제시한 고립운전 검출기법 및 해석결과를 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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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.