• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.75-81
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• 2008

The induction motor is given a great deal of weight on the industry generally. Therefore, the fault of the induction motor may cause the fault to effect another parts or another faults in the whole system as well as in itself. These are accompany with a lose of the reliability in the industrial system. Accordingly to prevent these situation, the scholars have studies the fault diagnosis of the induction motor. In this paper, we proposed the diagnosis system of the induction motor. The method of diagnosis in proposed system is extracted the feature of the current signal by the wavelet transform. These extracted feature is used the automatic discrimination system by the neural network. We experiment the automatic discrimination system using the three faults imitation that often generated in the induction motor. The proposed system have achieved high reliable result with a simple devices about the three faults.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.467-469
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• 2000

This paper describes an application of discrete wavelet transform for power transformer protection. It is shown that the moving wavelet coefficients method based feature extraction for discrimination between actual internal faults and energizing state. The simulations of power transformer have been carried out using EMTP. The proposed method is more effectively and simpler to distinguish internal faults from inrush currents.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1039-1041
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• 1998

This paper presents a practical fuzzy expert system to diagnose various faults occurred in local power systems. This integrated system can diagnose all faults occurred in a transmission network and substations. In this paper. the fuzzy reasoning of the diagnostic process is discussed in detail. The discrimination of false operations and non-operations of protective devices as well as the fault identification scheme are also analyzed together with the fuzzy inference process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1146-1151
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• 2018

Recently, researches on the intelligence of electric power facilities have been trying to apply artificial intelligence techniques as computer platforms have improved. In particular, faults occurring in substation should be able to quickly identify possible faults and minimize power fault recovery time. This paper presents fault location technique for 154kV substation using neural network. We constructed a training matrix based on the operating conditions of the circuit breaker and IED to identify the fault location of each component of the target 154kV substation, such as line, bus, and transformer. After performing the training to identify the fault location by the neural network using Weka software, the performance of fault location discrimination of the designed neural network was confirmed.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.363-366
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• 2007

This paper demonstrates a novel application of Fuzzy C-Mean(FCM) to identify the causes of ground faults in Transmission system. The discrimination scheme which can automatically recognize the fault causes is proposed using artificial neural networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.368-369
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• 2011

This article suggests an online-based remote fault current mode discrimination method in order to identify the causes of the power line faults with various causes. For that, it refers to existing cause identification methods and categorizes modes by fault causes based on statistical techniques beforehand and performs the pretreatment process of fault currents by each cause acquired from the fault recorder into a topological plane in order to extract the characteristics of fault currents by each cause. After that, for the fault mode categorization, it discriminates modes by each cause using data by each cause as leaning data through utilizing RBF network based on FCM allowing self-organization in deciding the middle layer. And then it tests the validity of the suggested method as applying it to the data of the actual fault currents acquired from the fault recorder in the electric power transmission center.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.86-88
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• 2009

This article suggests an online-based remote fault current mode discrimination method in order to identify the causes of the power line faults with various causes. For that, it refers to existing cause identification methods and categorizes modes by fault causes based on statistical techniques beforehand and performs the pre-treatment process of fault currents by each cause acquired from the fault recorder into a phase space in order to extract the characteristics of fault currents by each cause. After that, for the fault mode categorization, it discriminates modes by each cause using data by each cause as leaning data through utilizing RBF network. And then it tests the validity of the suggested method as applying it to the data of the actual fault currents acquired from the fault recorder in the electric power transmission center.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.243-245
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• 2004

This paper proposes the discrimination method for the fault location, whether it is within the line where the distributed generation(DG) is integrated or out of the line (but sharing the same bus of the substation). In general, DG has to be disconnected from the grid when the fault occurs on the interconnected distribution feeder as soon as possible. However, the faults occured on the neighboring feeder would mistakenly cause the disconnection of the DG. For reliable operation of DG, DG should be sustained at the fault occurred on neighboring distribution feeders. The proposed identification method utilizes the impedance monitored from the DG and examines the coordination of overcurrent relay of the distribution system. This paper describes how the proposed method to identify the faulted feeder and how the method can be utilized.

• English & American cultural studies
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• v.14 no.1
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• pp.311-335
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• 2014

Those who are damned in Light in August (1932) include Lena Grove, Joe Christmas as well as Gail Hightower. Through these characters, William Faulkner criticizes the confrontation between the North and the South after Civil War, religious fundamentalism, and racial discrimination which were great social issues in the twentieth century American society. The main characters are commonly isolated from the community through their grandfather's influence instead of father, which lets Americans understand that their faults originated from the beginning of America. Although they tend to approach to the community from their isolation, the damned are refused from the community. However, Faulkner would not lose his hope even on the ground of Christmas's death. By evoking from Hightower and Bunch their responses for good, Lena can draw Hightower into the community, and create her home with Bunch as a final victor. Even in the community being rampant with racial hatred, which most of Americans can not but face with, Faulkner can provide us with a ray of hope through these three characters.

• Journal of the Korean earth science society
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• v.44 no.3
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• pp.210-221
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• 2023

The purpose of this study is to evaluate the content knowledge and problem solving process used by pre-service earth science teachers while discriminating faults on geological maps. For this, we collected and evaluated data on fixation duration and gaze plot, while pre-service earth science teachers (N=12) solved the problem on faults interpretation using an eye tracker (Tobii Pro Glass 2 model). The results were as follows. First, most of the pre-service earth science teachers know the concepts of the normal and reverse fault but they do not know the procedural knowledge essential for fault interpretation on geological maps. Second, the pre-service earth science teachers did not draw a geological cross-sectional map to interpret the fault on the geological map and interpreted the fault based on two-dimensional information collected from the geological map rather than three-dimensional information. Therefore, it is essential to improve the teaching and learning environment so that pre-service earth science teachers who will become earth science teachers in the future can learn procedural knowledge essential to comprehend natural phenomena including understanding natural phenomena. The results of this study can substantially help organize a new earth science curriculum or develop materials on teachers' education in the future.