• Journal of Power Electronics
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• v.15 no.1
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• pp.235-245
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• 2015

This paper proposes a reactive current assignment and control strategy for a doubly-fed induction generator (DFIG) based wind-turbine generation system under generic grid voltage sag or swell conditions. The system's active and reactive power constrains during grid faults are investigated with both the grid- and rotor-side convertors (GSC and RSC) maximum ampere limits considered. To meet the latest grid codes, especially the low- and high-voltage ride-through (LVRT and HVRT) requirements, an adaptive reactive current control scheme is investigated. In addition, a torque-oscillation suppression technique is designed to reduce the mechanism stress on turbine systems caused by intensive voltage variations. Simulation and experiment studies demonstrate the feasibility and effectiveness of the proposed control scheme to enhance the fault ride-through (FRT) capability of DFIG-based wind turbines during violent changes in grid voltage.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.23-30
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• 2014

This paper presents an on-line diagnosis system for identifying health and faulted conditions in squirrel-cage induction motors using stator current, temperature, and partial discharge signals. The proposed diagnosis system can diagnose induction motor faults such as broken rotor bars, air-gap eccentricities, stator winding insulations, and bearing faults. Experimental results obtained from induction motors show that the proposed system is capable of detecting induction motor faults.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1057-1058
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• 2007

This paper proposes an alternative vibration detection method in a squirrel-cage induction motor using flux sensors. The air-gap flux will be changed when mechanical vibration occurs by bearing fault as well as broken rotor bar and air-gap eccentricity. For detecting those flux variations due to vibration, search coils are installed at stator slots. The induction motor with 380 [V], 7.5 [kW], 4 [Poles], 1,760 [rpm] ratings is used. Magnitudes and distortion of the induced voltage from flux sensors are used to discriminate faulted types. As a result, the flux sensor has been proven to be useful for vibration detection. It is compared to the result with vibration sensor as well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.492-498
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• 2016

It is well known that since abrupt faults in induction motors tend to lead to subsequent faults and deterioration of the drive apparatus, motor faults may lead to several operating restrictions, such as security problems and economic loss. A lot of research has been done in the area of diagnosis to detect machine faults and to prevent catastrophic hazards in the motor drive system. This paper presents a new method of motor current signature analysis in which the DC-link current of the inverter-driven induction motor system, where a single current sensor is employed instead of three AC current sensors, is measured, and fast Fourier transform analysis is performed. This proposed method makes it possible to easily discern and clearly separate the motor fault current signature from the normal operation current flowing through the stator and rotor windings.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.281-284
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.8
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• pp.131-137
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• 2005

The purpose of this paper is to evaluate the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algerian for the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency bases to detect the failure of the motor. Specific signature can be described in time and frequency domain for each faults of the motor. The spectrum of the signal was found more useful for the monitoring purpose. The supply voltage imbalance and tin to turn failure of the stator winding could be detected by analysing the specific sidebands of the axial flux and sideband of the rotor bar pass frequency with the high resolution spectrum. The goal of this study verity that the axial flux measurement for the induction motor is a powerful tool for the diagnostic method and develope the algorithm to detect the fault.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.111-116
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• 2022

The causes of motor burnout include overload, phase loss, restraint, interlayer short circuit, winding ground fault, instantaneous overvoltage, and the rotor contacting the stator, leading to insulation breakdown, leading to breakdown or electrical accidents. Therefore, equipment failure causes not only loss due to cost required for equipment maintenance/repair, but also huge economic loss due to productivity decrease due to process stop because the process itself including the motor is stopped. The current level of technology for diagnosing motor failures uses vibration, heat, and power analysis methods, but there is a limit to analyzing the problems only after a considerable amount of time has passed according to the failure. Therefore, in this paper, a device and algorithm for measuring insulation resistance using DC AMP signal was applied to an industrial motor to solve this problem. And by following the insulation resistance state value, we propose a diagnosis of deterioration and failure of the motor that cannot be solved by the existing method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.106-109
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• 2005

산업화와 더불어 자동화의 요구에 따라 여러 분야에서 유도전동기의 응용 사례가 늘어나고 있다. 본 연구는 인버터단의 전류센서에서 실시간 얻어진 전류신호로 부터 대표적인 전기, 기계적인 4가지 고장(Bearing Fault, Broken Rotor bar, Misalignment, Unbalance)을 검출하여 예기치 못한 고장에 대비할 수 있는 실시간 진단 알고리듬을 제시한다. 실시간 진단의 핵심요소인 동기화 방법으로서 Hilbert Transform을 응용하였다. 총 40세트의 정상 모터 데이터를 임의로 선택하여 두 데이터의 차를 이용하여 0에 근사한 정상 모터 뎀플릿 값을 설정하였다. 이를 이용하여 진단 대상 전동기의 고장 유무를 미리 판단하게 된다. 만약 기준치 이상의 오차가 나타나게 되면, 이와 비교하기 위한 미리 수집된 각4가지 고장 전동기의 미소신호 템플릿 데이터와의 유사성을 비교하여 고장의 종류를 표시하며, 고장의 종류가 진단되지 않은 대상은 고장 유무만 표시된다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.2
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• pp.189-194
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• 2017

An compressor that is combined with the rotor and pneumatic technology has been researching for the performance of pressure. However, the control of operations, an accurate diagnosis and the maintenance of compressor system are limited though the simple structure of compressor and compression are advantaged to reduce the energy. In this paper, the characteristic of the compressor operating under the normal or abnormal condition is realized. and the efficient diagnosis method is proposed through feature based analysis. Also, by using the GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature analysis is conducted. Different misalignment mode of learning data for compressor is evaluated using the fault simulator. Therefore, feature based analysis is conducted considering misalignment mode of the compressor and the possibility of a diagnosis of misalignment is evaluated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.791-796
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• 2012

This paper presents the various analysis of the power system for operating the new SIHWA tidal power plant. In the analysis of the power system, summer load condition of 2011 is used. Especially, power flow, fault current, voltage and contingency of SIHWA tidal power plant area are analyzed by using PSS/E and there is no problem for the dynamic stability simulation. The new SIHWA tidal power plant is located in near metropolitan area where about 43% amount of the system load is consumed. Therefore, transmission losses are reduced. In addition, system marginal price can be lowered by generating the new SIHWA tidal power plant. The generation pattern of the SIHWA tidal plant is analyzed and the changes of generation are presented for various water levels by control of the rotor angle alpha and beta in water wheel generator.