• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.356-362
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• 2009

The ELectromagnetic Core Imperfection Detector (EL-CID) test was performed on a small generator in the laboratory and a gas turbine generator in the field to assess the fault condition of generator stator core. Artificial defects with six different sizes were introduced in the small generator. The scan results on six defects show a very large increase in the magnitude of fault current compared to that obtained with a healthy core. After the stator core heats up, a thermal imaging camera was used to detect hot spot on the inner surface of the core for comparison. Several faults were found during inspection of the gas turbine generator with the EL-CID. It has been shown that the existence of a fault can be determined by monitoring the magnitude of fault current.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.97-100
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• 2004

This paper introduced an artificial fault generator which was operated in the Go-Chang field-test center and explained the result of single line-to-ground fault by AFG. The AFG can basically experiment line-to-line and line-to-ground faults. This facility directly connected distribution transmission lines, so the test results are very useful for power system analysis and protection. Using the function of the AFG, we briefly said the test methods ud results for the 22.9[kV-v] and $6.6[kV-{\Delta}]$ system.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1126-1129
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• 1999

In this paper theoretical review for the design and the algorithm of Artificial Fault Generator, on the power distribution center is which able to purposely generate and get rid of fault with the view of testing distribution systems including switchgears, was made. For the following paper verification with EMTP will be performed in order to review the function and the algorithm so that the optimized design can be established.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• Journal of Power Electronics
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• v.15 no.6
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• pp.1664-1672
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• 2015

This paper proposes a series arc fault detection algorithm which incorporates peak voltage and harmonic current detectors for single-phase boost power factor correction (PFC) rectifiers. The series arc fault model is also proposed to analyze the phenomenon of the arc fault and detection algorithm. For arc detection, the virtual dq transformation is utilized to detect the peak input voltage. In addition, multiple combinations of low- and high-pass filters are applied to extract the specific harmonic components which show the characteristics of the series arc fault conditions. The proposed model and the arc detection method are experimentally verified through a boost PFC rectifier prototype operating under the grid-tied condition with an artificial arc generator manufactured under the guidelines for the Underwriters Laboratories (UL) 1699 standard.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.7
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• pp.37-44
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• 2013

Phase to ground faults are possibly one of the maximum number of faults in power distribution system. During a ground fault the maximum fault current and neutral to ground voltage will appear at the pole nearest to the fault. Distribution lines are consisted of three phase conductors, an overhead ground wire and a multigrounded neutral line. In this paper phase to neutral faults were staged at the specified concrete pole along the distribution line and measured the ground fault current distribution in the ground fault current, three poles nearest to the fault point, overhead ground wire and neutral line. A simplified equivalent circuit model for the distribution system under case study calculated by using MATLAB gives results very close to the ground fault current distribution yielded by field tests.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1818-1821
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• 2008

Several off-line diagnostic tests which include the insulation resistance(IR), polarization index(PI), low-voltage AC, and recurrent surge oscillograph(RSO) tests were performed to assess the condition of generator rotor windings. The low-voltage AC and the RSO tests were performed on the gas turbine generator rotor winding to detect shorted turns. Before intentionally applying artificial shorted faults, it was confirmed by the low voltage AC and the RSO tests that the winding was in sound condition. For simulated shorted rotor winding turns, the RSO test detected the fault in the winding. The RSO test was capable of identifying the number and pole location of the shorted turns for a number of simulated shorted coils.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.