• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.538-544
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• 1999

In this paper, we propose a new fault detection algorithm for transmission line protection using ANFIS(Adaptive Network Fuzzy Inference System). The developed system consists of two subsystems: fault type classification, and fault location estimation. We use rms value, zero sequence component and positive sequence of current, and then using learning method of neural network, premise and consequent parameters are tuned properly. To prove the performance of the proposcd system, generated data by EMTP(Electr0- Magnetic Transient Program) sin~ulationi s used. It is shown that the proposed relaying classifies fault types accurately and advances fault location estimation.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.165-166
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• 2005

In this paper, we investigated the quench generation of HTSC elements in fault types according to inductance variation in the integrated three-phase flux-lock type SFCL. The integrated three-phase flux-lock type SFCL was the upgrade version of the single-phase flux-lock type SFCL. The structure of the integrated three-phase flux-lock type SFCL consisted of three-phase flux-lock reactor wound on an iron core with the ratio of the same turn between coil 1 and coil 2 in each phase. When the SFCL is operated under the normal condition, the flux generated in the iron core is zero because the flux generated between two coils of each single phase is canceled out. Therefore, the SFCL's impedance is zero, and the SFCL has negligible influence on the power system. However, if a fault occurs in any single-phase among three phases, the flux generated in the iron core is not zero any more. The flux makes HTSC elements of all phases quench irrespective of the fault type, which reduces the current of fault phase as well as the current of sound phase. It was observed that the fault current limiting characteristics of the suggested SFCL were dependent on the quench characteristics of HTSC elements in all three phases.

• Smart Structures and Systems
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• v.20 no.1
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• pp.43-52
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• 2017

Structural health monitoring has drawn great attention in the field of civil engineering in past two decades. These structural health monitoring methods evaluate structural integrity through high-quality sensor measurements of structures. Due to electronic deterioration or aging problems, sensors may yield biased signals. Therefore, the objective of this study is to develop a fault detection method that identifies malfunctioning sensors in a sensor network. This method exploits the autoregressive modeling technique to generate a bank of Kalman estimators, and the faulty sensors are then recognized by comparing the measurements with these estimated signals. Three types of faults are considered in this study including the additive, multiplicative, and slowly drifting faults. To assess the effectiveness of detecting faulty sensors, a numerical example is provided, while an experimental investigation with faults added artificially is studied. As a result, the proposed method is capable of determining the faulty occurrences and types.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1486-1491
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• 1999

This paper presents a method of recognizing high impedance fault(HIF) of electrical power systems and classifying fault patterns based on chaos attractors. Two dimensional chaos attractors are reconstructed from neutral point current waveforms. Reliable features for HIF pattern classification are obtained from the chaos attractors. Radial basis function network, trained with two types of HIF data generated by the electromagnetic transient program and measured form actual faults. The RBFN successfully classifies normal and the three types of fault patterns according to the features generated from the chaos attractors.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.12
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• pp.573-579
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• 2005

Dynamic analysis of a transmission line which is compensated by a FACTS device such as STATCOM, SSSC and UPFC is carried out in this paper and the impacts on conventional line protection methods such as the DCPM (Differential Current Protection Method) and the DPM (Distance Protection Method) are reviewed. A refined DCRM is proposed to detect faults properly regardless of the FACTS operation. The proposed method is applied to a FACTS-compensated line with a variety of faults and is verified by simulation results. An adaptive DPM on a FACTS-compensated line was proposed previously in the literature. In order to emphasize the necessity of the modified DPM, the conventional DPM is applied to a FACTS-compensated system. Significant factors such as fault types, fault locations, and fault resistances as well as FACTS device types are considered for relaying setting.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.252-261
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• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.62-69
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• 2009

For the fault diagnosis of power transformer, we develop a diagnosis algorithm based on support vector machine. The proposed fault diagnosis system consists of data acquisition, fault/normal diagnosis, and identification of fault. In data acquisition part, concentrated gases are extracted from transformer for data gas analysis. In fault/normal diagnosis part, KEPCO based decision rule is performed to separate normal state from fault types. The determination of fault type is executed by multi-class SVM in identification part. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.873-877
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• 2016

This paper proposed the structure that applied superconductors to the neutral line of a transformer and applied the normal conductors to the third line. The superconductor applied to the neutral line of a transformer limited the peak value of initial fault current, while the normal conductor finally limited the fault current. In order to secure the operating reliability of transformer type Superconducting Fault Current Limiter (SFCL) of previously proposed structure, we analyzed the operating characteristics according to the fault types. We tested a line-to-ground fault and a line-to-line fault. As a result of the experiment, all the faults showed that the superconductor stably limited the peak-value of initial fault current. Also, the normal conductor finally limited the fault current. Based on this research results, We thought that if the structure of inserting superconductor into the neutral line is applied to the real system, it could improve the reliability and stability of the power system.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.859-868
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• 2012

A new fault location algorithm based on stationary wavelet transform and its verification experiment results are described for HVDC submarine cables in this paper. For wavelet based fault location algorithm, firstly, 4th level approximation coefficients decomposed by wavelet transform function are superimposed by correlation, then the distance to the fault point is calculated by time delay between the first incident signal and the second reflected signal. For the verification of this algorithm, the real experiments based on various fault conditions and return types of fault current are performed at HVDC submarine cable test yard located in KEPCO(Korea Electric Power Corporation) Power Testing Center of South Korea. It proves that the fault location method proposed in this paper is very simple but very quick and accurate for HVDC submarine cable fault location.