• Proceedings of the KIEE Conference
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• summer
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• pp.619-621
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• 2004

This paper presents the rapid and accurate algorithm for fault discrimination in transmission lines. When faults occur in transmission lines, fault discrimination is very important. If high impedance faults occur in transmission lines, it cannot be detected by overcurrent relays. The method using current and voltage cannot discriminate high impedance fault. Because of this reason this paper uses voltage and zero sequence current, and the proposed algorithm uses fuzzy logic method. This algorithm uses voltage and zero sequence current per period in case of faults. Single line ground fault and three-phase fault can be detective using voltage. Two-line ground fault and line to line fault and high impedance can be detected using zero sequence current. To prove the performance of the algorithm, it test algorithm with signal obtained from ATPDraw simulation.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.3
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• pp.116-122
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• 2006

The underground transmission lines is continuously expanded in power systems. Therefore the fault of underground transmission lines are increased every year because of the complication of systems. However the studies dealing with fault location in the case of the underground transmission lines are rarely reported except for few papers using traveling wave method and calculating underground cable impedance. This paper describes the algorithm using fuzzy system and travelling wave method in the underground transmission line. Fuzzy inference is used for fault discrimination. To organize fuzzy algorithm, it is important to select target data reflecting various underground transmission line transient states. These data are made of voltage and average of RMS value on zero sequence current within one cycle after fault occurrence. Travelling wave based on wavelet transform is used for fault location. In this paper, a variety of underground transmission line transient states are simulated by EMTP/ATPDraw and Matlab. The input which is used to fault location algorithm are Detail 1(D1) coefficients of differential current. D1 coefficients are obtained by wavelet transform. As a result of applying the fuzzy inference and travelling wave based on wavelet transform, fault discrimination is correctly distinguished within 1/2 cycle after fault occurrence and fault location is comparatively correct.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.2
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• pp.134-141
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• 2003

The power transformer is one of the very important electric facilities in power systems. Recently, current differential relay is widely used to protect such power transformer But if inrush occurs in transformer, relay can be tripped by judging like internal fault. Therefore the correct discrimination between internal winding fault, inrush and overexcitation should be performed. This paper presents a new protective relaying algorithm which discriminates inrush, internal faults and overexcitation of transformer modelled using BCTRAN and HYSDAT of EMTP. Discrimination between internal winding fault and inrush is revealed in simulation within 1/2 cycle after fault. Accordingly, it is evaluated that the proposed algorithm has better discrimination characteristics in various cases thin the current relaying for protection of transformer.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.2
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• pp.114-120
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• 2003

According to the use of distribution automation systems, the function to find or to search a fault phase is necessary for automatic switches in a distribution substation. In this paper, two algorithms are developed to fine the fault circuit and the fault phase for the automatic switches in substation with ungrounded power system. One is the fault circuit searching method using the zero sequence voltage at the bus and zero sequence current of circuit current and the other is to find the fault phase using the line voltage and zero sequence current. The developed algorithms are tested in the case study simulations. An ungrounded power system is modeled by EMTP as a case study system. The developed algorithms are tested in the case study simulations and each shows correct results.

• 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.

• 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.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.412-415
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• 2003

It is important to classily fault types, discriminate fault section and calculate the fault location by any detecting technique for combined transmission lines. This paper proposes the technique to classily the fault types and fault section using neuro-fuzzy systems. Neuro-fuzzy systems are composed of three parts to perform different works. First, neuro-fuzzy system for fault type classification is performed with approximation coefficient of currents obtained by wavelet transform. The second neuro-fuzzy system discriminates the fault section between overhead and underground with detail coefficients of voltage and current. The last neuro-fuzzy system calculates the fault location with impedance in this paper, neuro-furry system shows the excellent results for classification of fault types and discrimination of fault section.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.534-536
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• 2003

It is important to classily fault types and discriminate fault section by any detecting technique for combined transmission lines. This paper proposes the technique to classify the fault types and fault section using neuro-fuzzy systems. Neuro-fuzzy systems are composed of two parts to perform different works. First, neuro-fuzzy system for fault type classification is performed with approximation coefficient of currents obtained by wavelet transform. Another neuro-fuzzy system discriminates the fault section between overhead and underground with detail coefficients of voltage and current. In this paper, neuro-fuzzy system shows the excellent results for classification of fault types and discrimination of fault section.