• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.307-309
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• 2008

This study suggests a modeling of grid-connected wind turbine generation system that has induction generator, and aims to perform simulations for outputs by the variation of actual wind speed and for fault current of wind generation system by the transformer winding connection. This study is implemented by matlab&simulink. The simulation shall be performed by assuming single line to ground fault generated in the system. Generator power, generator rotor speed, generator terminal current and fault current shall be observed following the performance of simulation. The fault current change will be dealt through the simulation results for fault current of wind generation system following the grid-connected transformer winding connection and the simulation result by the transformer neutral ground method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.225-226
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• 2005

In this Study, we have acquired 5-simulation Fault types Signals of high voltage Motor stator winding using epoxy/mica coupler. In order to know stator winding fault type using fault signals, we have performed feature extraction to apply wavelet transform technique. we have obtained skewness and kurtosis as statistical parameters of fault signal pattern from non deterioration state winding. We have know that 5 fault signals types have done an exponential function pattern shape but individually fault a class widely was different each other a signal waveform of pattern.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1183-1190
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• 2007

This paper proposes the improved fault decision algorithm using DWT(Discrete Wavelet Transform) and ANNs for the FRTU(Feeder Remote Terminal Unit) on the feeder in the power distribution system. Generally, the FRTU has the fault decision scheme detecting the phase fault, the ground fault. Especially FRTU has the function for 2000ms. This function doesn't operate FI(Fault Indicator) for the Inrush current generated in switching time. But it has a defect making it impossible for the FI to be operated from the real fault current in inrush restraint time. In such a case, we can not find the fault zone from FI information. Accordingly, the improved fault recognition algorithm is needed to solve this problem. The DWT analysis gives the frequency and time-scale information. The neural network system as a fault recognition was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault recognition algorithm is improved by using voltage monitoring system, DWT and neural network. All of the data were measured in actual 22.9kV power distribution system.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.859-866
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• 2001

Recently, fault injection techniques are used for evaluation of the fault coverage properties of safety-critical systems. This paper describes the TCN Fault Injector(TFI) implemented for TCN safety analysis. The implemented TFI injects network level faults to Intelligent MVB Controller that is designed for the Korean High Speed Train. With TFI, it can be verified whether the MVB controller meets TCN specification and its safety requirements.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.723-726
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• 1998

Fault dropping is a very important part of test generation process. It is used to reduce test generation time. Test generation systems use fault simulation for the purpose of fault dropping by identifying detectable faults with generated test patterns. Two kinds of delay fault model is used in practice, path delay fault model and gate delay fault model. In this paper we propose an efficient method for gate delay test generation which shares second test vector.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2102-2107
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• 2008

This paper demonstrates a enhanced FCM to identify the causes of ground faults in power distribution systems. The discrimination scheme which can automatically recognize the fault causes is proposed using Fuzzy RBF networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.219-230
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• 2006

Since the key issue that 'the Yangsan fault is seismically an active fault" was raised in 1983, thegeological and geomorphological studies of active fault have been made by many researchers. These studies are mainly focused on the Yangsan fault system(YFS) and Ulsan fault system(UFS) due to many historical earthquakes occurred in this area. There are two different types of active faultings under the ENE-WSW horizontal stress field in the southeastern part of the Korean Peninsula. The NNE-trending YFS is the most prominent right-lateral strike-slip fault and has a continuous trace about 200 km long. Some part of this system has been active during the late Quaternary with evidences clearly recognized on both the northern (Yugyeri and Tosung-ri areas) and southern parts (Eonyang to Tongdosa area) of the YFS. in the southern part, the estimated vertical slip rate is about 0.02 - 0.07 mm/yr, and the lateral slip rate may be several times larger than the vertical rate. The most recent event occurred prior to deposition of Holocene alluvium, in the northern part, the fault trend locally changes to almost N-S, dips to the east and has reverse movement. The average vertical slip rate is estimated to be less than 0.1 mm/yr. The most recent event probably occurred after 1314 years BP (AD 536). The NNW-SSE (or N-S) trending UFS is a predominantly reverse fault that built up U-ie eastern mountain and has been active during U-ie late Quaternary. The fault trace is not straight but irregularly undulates along the foot of the mountain on the east. From the disturbed terraces along U-ie fault, the average vertical slip rate on U-iis system is estimated to be about 0.08.13mm/yr. The latest event is not well studied, but seems to have occurred after the last glacial maximum in the Malbang fault and 14,000 years BP in the Kalgok fault of the UFS. However, important issues such as fault segmentation, recurrence interval, age of Quaternary deposits need further studies.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.11
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• pp.735-742
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• 2017

In this study, an effective model-based sensor fault detection methodology that can detect and isolate PEM temperature sensors fault is introduced. In fuel cell vehicle operation process, the stack temperature affects durability of a fuel cell. Thus, it is important for fault algorithm to detect the fault signals. The major objective of sensor fault detection is to guarantee the healthy operations of the fuel cell system and to prevent the stack from high temperature and low temperature. For the residual implementation, parity equation based on the state space is used to detect the sensors fault as stack temperature and coolant inlet temperature, and residual is compared with the healthy temperature signals. Then the residuals are evaluated by various fault scenarios that detect the presence of the sensor fault. In the result, the designed in this study fault algorithm can detect the fault signal.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.69-84
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• 2019

The purpose of this study is to extract lineaments in the southeastern part of the Gaeum Fault System, and to understand their characteristics and a relationship between them and fault activation. The lineaments were extracted using a multi-layered analysis based on a digital elevation model (5 m resolution), aerial photos, and satellite images. First-grade lineaments inferred as an high-activity along them were classified based on the displacement of the Quaternary deposits and the distribution of fault-related landforms. The results of classifying the first-grade lineaments were verified by fieldwork and electrical resistivity survey. In the study area of 510 km², a total of 222 lineaments was identified, and their total length was 333.4 km. Six grade lineaments were identified, and their total length was 11.2 km. The lineaments showed high-density distribution in the region along the Geumcheon, Gaeum, Ubo fault, and a boundary of the Hwasan cauldron consisting the Gaeum Fault System. They generally have WNW-ESE trend, which is the same direction with the strike of Gaeum Fault System. Electrical resistivity survey was conducted on eight survey lines crossing the first-grade lineament. A low-resistivity zone, which is assumed to be a fault damage zone, has been identified across almost all survey lines (except for only one survey line). The visual (naked eyes) detecting of the lineament was evaluated to be less objectivity than the automatic extraction using the algorithm. However, the results of electrical resistivity survey showed that first-grade lineament extracted by visual detecting was 83% reliable for inferred fault detection. These results showed that objective visual detection results can be derived from multi-layered analysis based on tectonic geomorphology.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.