• 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.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.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.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.3
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• pp.411-416
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• 2011

In distribution system, to enhance the reliability of a HIF (High Impedance Fault) determination, it is very important to obtain the HIF model which can represent accurately the HIF' characteristics. Accordingly, in this paper, the several HIF models which are based on the arc models are reviewed, and then the characteristics of the HIF models are compared from the reviewed results. Finally, a Methodology implementing the arc model is studied based on the direct integration method.

• 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 Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1388-1393
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• 1999

A more reliable algorithm for detecting a high impedance fault (HIF) requires fault currents at the relaying point containing information of load condition as well as HIF characteristics. This paper presents a modeling method of an HIF in a distribution system using EMTP. From the voltage and current waveforms of HIF experiment, the voltage-current characteristic is obtained and then piecewise linearized. The proposed method gets several points on the linearized voltage-current curve and then represents nonlinearity as piecewise linear resistances using Transient Analysis of Control Systems (TACS) in EMTP. Thus, an HIF is represented as a voltage source in the first and third quadrants of voltage-current plane. The method is implemented in EMTP and thus the voltage and current at the relaying point can be obtained when an HIF occurs. In this paper, an HIF was simulated on various load conditions and fault conditions in 22.9 [kV] distribution systems.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1373-1375
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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, equiment damage or personal threat. This paper proposes the model of HIF in transmission line using the ZnO arrester and resistance to be implemented within EMTP. Wavelet transform is efficient and useful for the detection of HIF in power system, because it uses variable windows according to frequency. HIF detection method using wavelet transform can distinguish HIF from similar phenomena like arcfurance load, capacitor bank switching and line switching.

• 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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• 1997.07c
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• pp.883-885
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• 1997

High impedance fault(HIF) is random in its behavior even in a similar environment. The detection of Ire HIF has focused on the development of algorithms based on harmonic, parameters of the arc currents. However, a fact that proper selection of the harmonic parameters, rather than algorithm selection, is more important is shown in this paper by applying three different performance evaluation methods on two HIF detection algorithms using eight harmonic parameters.