• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.16-21
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• 1999

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault test, which was carried in Korean electric power systems, it was found that a arcing phenomenon occurred during the high level portion of conductor voltage in each cycle. In this paper, we propose a new method for detection of high impedance faults, which uses the arcing fault current difference during high voltage and low voltage portion of conductor voltage waveform. To extract this difference, we diveded one cycle fault current into equal spanned four data windows according to the magnitude of voltage waveform and applied fast fourier transform(FFT) to each data window. The frequency spectrum of current wavefrom in each portion are used as the inputs of neural network and is trained to detect high impedance faults. The proposed method shows improved accuracy when applied to staged fault data and fault-like load.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.1274-1279
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• 2004

This paper proposes technique to detect ground fault in railway high voltage distribution lines. Overcurrent relay technique is widely used for detecting one line ground fault that occurs most frequently in railway high voltage distribution lines. However, ground fault in distribution line is usually high impedance fault with arc. Because the fault current magnitude measured in substation is very small, the conventional overcurrent relay can't detect the high impedance ground fault. Therefore this paper proposes the advanced technique using wavelet transform. It extracts D1 component from fault signals and detects fault comparing magnitude of D1 component in each phase. To evaluate this proposed technique, we model distribution system using PSCAD/EMTDC and extract various fault data. In conclusion this technique can detect ground fault including high impedance fault regardless of fault distance, fault impedance etc.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1492-1497
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• 1999

High impedance fault(HIF) is defined as fault the general overcurrent relay can not detect or interrupt. Especially when HIF occur in residential areas, energized high voltage conductor results in fire hazard, equipment damage or personal threat. This paper proposes the model of the high impedance fault in transmission line using the ZnO arrester and resistance to be implemented within EMTP. The performance of the proposed model is tested on a typical 154[kV] korean transmission line system under various fault conditions. Wavelet transform is efficient and useful for the detection of high impedance fault in power system, because it uses variable windows according to frequency. In this paper, HIF detection method using wavelet transform can distinguish HIF form similar fault like arcfurance load, capacitor bank switching and line switching.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.369-373
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• 2000

High impedance fault(HIF) is defined as a fault that the general overcurrent relay can not detect or interrupt. Especially when HIF occurs in residential areas, energized high voltage conductor results in fire hazard, equipment damage or personal threat. This paper proposes a fault location estimation algorithm for high impedance fault using wavelet transform. The algorithm is based on the wavelet analysis of the fault voltage and current signals. The performance of the proposed algorithm is tested on a typical 154kV korean transmission line system under various fault conditions. From the tests presented in this paper it can be concluded that a fault location estimation algorithm using wavelet transform can precisely calculate the fault point for HIF.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.426-435
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• 1999

A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.212-212
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• 1999

This paper proposed a high impedance fault detection method using a neural network on distribution lines. The v-I characteristic curve was obtained by high impedance fault data tested in various soil conditions. High impedance fault was simulated using EMTP. The pattern of High Impedance Fault on high density pebbles was taken as the learning model, and the neural network was valuated on various soil conditions. The average values after analyzing fault current by FFT of evenr·odd harmonics and fundamental rms were used for the neural network input. Test results were verified the validity of the proposed method.

• Progress in Superconductivity and Cryogenics
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• v.3 no.1
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• pp.40-44
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• 2001

Recently. the high-tc superconducting fault col-rent limiters (SFCL) are studied worldwide to be classified as a resistive type or an inductive type such as a magnetic shielding type and a inductive type. The high-tc SFCL wish an open core belongs to the magnetic shielding type SFCL. Unlike conventional magnetic shielding type SFCLS it uses the open core to reduce the mechanical vibrations and installation space, The high-tc SFCL with an open core was designed and manufactured by stacking three BSCCO 2212 tubes. It was tested in the maximum source voltage of 400 Vrms. The results such as the reduction of fault current and impedance of the SFCL are described in this paper. The results show that the fault current in the source voltage of 400 Vrms was reduced to be about 123 Apeak. about 3.9 times greater than the normal state current. Also, the impedance of the high-tc SFCL was about 9${\Omega}$ about 9 times greater than the normal state impedance. The impedance of the SFCL appears just after the fault, and its size is dependent on the source voltage. From the impedance, the inductance of the SFCL was calculated.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.924-926
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• 1997

A high impedance fault(HIF) in a power system could be due to a downed conductor, and is a dangerous situation because the current may be too small to be detected by conventional means. In this paper, HIF(High impedance fault) and LIF(Low impedance fault) detection methods were reviewed. No single defection method can detect all electrical conditions resulting from downed conductor faults, because high impedance fault have arc phenomena, asymmetry and randomness. Neural network are well-suited for solving difficult signal processing and pattern recognition problem. This paper presents the application of artificial neural network(ANN) to detect the HIF and LIF. Test results show that the neural network was able to identify the high impedance fault by real-time operation. Furthermore, neural network was able to discriminate the HIF from the LIF.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2525-2527
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• 2000

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device operating, so it is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents detections in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. Wavelet transform analysis is applied the time-scale information to fault signal. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.105-111
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of discrete wavelet transform to the various HIF data. These data were measured in actual 22-9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.