• 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 KIEE Conference
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• 1996.07b
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• pp.811-813
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• 1996

High impedance fault can not be easily detected by conventional method. But if it would not be detected and cleared quickly, it can result in fires, and electric shock. In this paper, neural network, which has learning capability, is used for high impedance fault detector. The potential of the neural network approach is demonstrated by simulation using KEPCO's measured data. The instantaneous values and frequency spectrum of current are respectively used as the inputs of neural networks. Also, the methods using combined data to exploit the advantage of each data are proposed. In this paper, back-propagation network(BPN) is used for high impedance fault detector and can use for high speed relay because it detects faults within 1 cycle.

• 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.

• 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.1015-1017
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• 1997

A high impedance fault on the multi-grounded three-phase four-wire distribution system can not be detected by conventional overcurrent sensing devices. In this paper, the neural network is used to detect high impedance faults. The proposed algorithm using back - propagation neural network is demonstrated by simulation with the staged fault test data. The harmonic components of current and the phase of voltage are used as the inputs of neural network. Results of the simulation can be used as a reference for the development of a high impedance fault detector.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2228-2230
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• 2002

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the Lifting 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 lifting scheme 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 KIEE Conference
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• 2001.07d
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• pp.2184-2186
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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 fast 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 fast 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.