• 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.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.

• 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.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.463-468
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• 1987

In this paper, a high impedance fault detection on 22.9kV multigrounded distribution system that has been very difficult by any existing conventional protective relaying systems is studied. Because the fault current is very low, it cannot be distinguished from neutral current caused by load unvalanced on multigrounded distribution system. We developed the new and best algorithms of high impedance ground fault detection. This algorithms are 'the even order power method, even order ratio method', 'and even order ratio varience method'. Using this algorithms, a detection device for high impedance faults is constructed and tested in the laboratory. And continually, it is installed and has been tested in KEPCO substations.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.405-407
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• 1994

In order to provide reliable power service and to prevent a potentail hazard and damage, it is important to detect high impedance fault in power distribution line. This paper presents a neural network based approach for the detection of high impedance faults. A time delay neural network has been selected and trained for the fault currents obtained from field experiments. Detection experiments have been performed with the data from four different high impedance surfaces. Experimental results indicated the feasibility of using TDNN for the detection of high impedance faults.

• 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.

• 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.

• 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.

• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.80-87
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• 1999

This paper proposed a high impedance fault detection method using a neural network on distribution lines. The $\upsilon-i$ 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 evaluated on various soil conditions. The average values after analyzing fault current by FFT of even.odd harmonics and fundamental rms were used for the neural network input. Test results were verified the validity of the proposed method .ethod .