• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1532-1537
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• 2009

Nowadays the diversity and large-capacity of electric appliances are strong effect on electrical fires augment in an alarming way. But, as the inactive response characteristics of the existing RCD (Residual Current protective Device) used on low voltage power distribution lines, so control of overload and electric short circuit faults, major causes of electrical fires, are not enough. Therefore this paper is confirmed the unreliability of the existing RCD by electrical faults simulation and is proposed a auxiliary trip device of RCD by using a high precision current sensor (namely, reed switch) for the prevention of electrical disasters in low voltage power distribution lines caused by overload or electric short circuit faults. The sensitive reed switch in the proposed ATD (auxiliary trip device) exactly detects the increased magnetic flux with the overload or the short current caused by a number of electrical faults, and then rapidly cuts off the existing RCD. The proposed auxiliary trip device of RCD is confirmed the excellent characteristics in response velocity and accuracy in comparison with the conventional circuit breaker through various operation performance analysis. The proposed ATD can also prevent electrical disaster, like as electrical fires, which resulted from the malfunction and inactive response characteristics of the existing RCD.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.801-803
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• 2003

Faults in induction motors can be categorized into mechanical faults and electrical faults, and most mechanical faults result from inferiority or damage of the bearing, while most electrical faults derive from insulation faults of stator windings and rotor bar cracks. When a crack appears on the rotor bar, its efficiency decreases, which increases energy consumption and temperature, reducing the life span of the motor. This kind of fault can only be sensed by the protection relay after the condition has worsened to a certain degree, bringing massive economic loss. This paper will deal with the diagnosis method of rotor bar faults through the load current analysis method of the motor used during operation.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.140-145
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• 2010

The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.405-411
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper propolsed the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.819-821
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• 1998

This paper deals with a method or a residual generation for fault isolation in a centrifugal pump with a water circulation system, driven by a speed controlled dc motor. It is based on parity relations derived from the moving-average model of the system and is used to identify sensor faults and two possible brush and impeller faults, where the former is dealt with additive faults, while the latter characterized as discrepancies between the nominal and actual plant parameters of the system is modelled by multiplicative faults. We will represent the propagation of this uncertainty to the model matrices by the approximate handling of partial derivatives of polynomials. With multiplicative faults, the transformation matrix implemented in the residual generator are calculated on-line. The simulation studies demonstrate that small changes of the system can be detected and diagnosed by using the method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1657-1662
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• 2017

Rotor bar faults in induction machines, which are a part of main distribution of power system, can even stop the entire system by causing contact between a stator and a rotor. There are two methods of diagnosing rotor bar faults in induction motors, online and offline tests, and existing diagnosis methods have many limitations which can lead to misdiagnosis. This paper proposes a potable rotor bar faults diagnosis system based on single phase rotation test, one of offline test methods, which detects rotor bar faults through impedance interpretation by exciting AC current in a stator winding. The test was conducted on a motor of 0.4kW in the laboratory and a motor of 1500kW in industry field.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1186-1192
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• 2016

This paper proposes techniques to diagnose short-circuit faults of both the diodes and power FET in switching mode power supply (SMPS) by using a simple analog tester. The diodes in full-bridge rectifier, power FET, switching transformer, and some sensors are modelled with resistor. The total resistance value measured at the input terminal of a SMPS is analyzed when the short-circuit faults of diodes in a full bridge rectifier or power FET are occurred. The short-circuit faults of one or two diodes in a full bridge rectifier, power FET, and both the diodes in a full bridge rectifier and power FET can be detected by a range of total resistance, which is measured by the analog tester. Through experiments, the theoretical analysis for total resistance under short-circuit faults can be verified.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2453-2461
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• 2009

Faults caused by medium-voltage customers have been increased and enlarged their portion in total distribution faults even though we have done many efforts. In the previous paper, we suggested the fault prediction model and fault prevention method for these distribution line faults. However we can't directly apply this prediction model in the field. Because we don't have an useful program to predict those customers causing distribution line faults. This paper presents the construction method of data warehouse in ERP system and the program to find customers who cause distribution line faults in medium-voltage customer's electric facility management applying data mining techniques. We expect that this data warehouse and prediction program can effectively reduce faults resulted from medium-voltage customer facility.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1116-1121
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• 2013

Mechanical imbalances are common mechanical faults in induction motors. Vibration monitoring techniques have been widely used for the diagnosis of mechanical faults in induction motors, but electrical detection methods have been preferred in recent years. For many years, researchers have concentrated on the Motor Current Signature Analysis (MCSA). This paper examines the effect of mechanical imbalances to induction machine electrical parameters. Instantaneous Power Signature Analysis (IPSA) technique used to detect these faults. In the paper, a full analysis of the proposed technique is presented, and experimental results for healthy and faulty motors have been shown and discussed.