• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.12
• /
• pp.1486-1491
• /
• 1999

This paper presents a method of recognizing high impedance fault(HIF) of electrical power systems and classifying fault patterns based on chaos attractors. Two dimensional chaos attractors are reconstructed from neutral point current waveforms. Reliable features for HIF pattern classification are obtained from the chaos attractors. Radial basis function network, trained with two types of HIF data generated by the electromagnetic transient program and measured form actual faults. The RBFN successfully classifies normal and the three types of fault patterns according to the features generated from the chaos attractors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.311-314
• /
• 1997

This paper presents recognition of high impedance fault patterns based of chaotic features using the Radial Basis Function Network(RBFN). The chaos attractor is reconstructed from the fault current data for pattern recognition. The RBFN successfully classifies the three kinds of fault pattems and one normal pattem.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.476_477
• /
• 2009

This paper compares the several cases with High Impedance Fault(HIF) conditions and analyzes the impact of the grid-connected photovoltaic system at the HIF conditions. Simulations are conducted by using Electro-Magnetic Transient Program (EMTP) and using the results, the analysis is presented.

• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2272-2274
• /
• 1998

This paper presents recognition and classification of high impedance fault(HIF) patterns in the electrical power systems based on chaotic features. Chaotic features are obtained from two dimensional chaos attractors reconstructed from fault current waveform. The RBFN is trained with the two types of HIF data generated by the electromagnetic transient program and measured from actual faults. The RBFN successfully classifies normal and the three types of fault patterns based on the binary chaotic features.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.640-642
• /
• 1995

This paper describes the present state and development prospect on the protective relaying under high resistance earth faults in transmission systems. Especially it is difficult to detect the fault accompanied with high resistance contary to low resistance. In the complicated power system if the detection is failed, power failure will be occured in large area. New technology with respect to such a problem must be developed. This paper introduces research and development trend in home and abroad.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1353-1355
• /
• 1996

This paper presents classification of high impedance fault pattern using linear prediction coefficients. A feature of neutral phase current is extracted by the linear predictive coding. This feature is classified into faults by a multilayer perceptron neural network. Neural network successfully classifies test data into three faults and one normal state.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1413-1415
• /
• 1999

배전선로상에서는 상 불평, 고저항 지락사고나 선로탈락이 발생할 수 있다. 또한, 고장 감지기 정보의 불확실성 등으로 배전 SCADA 정보로부터 정확한 사고유형과 사고위치를 확인하는 작업은 매우 어렵다. 따라서 본 연구에서는 배전선로상에서 발생할 수 있는 다양한 사고들에 대해 사고유형과 사고발생 위치를 신속하고 정확하게 추론할 수 있는 전문가 시스템을 제안한다. 전문가 시스템은 배전 SCADA기능과 수집된 데이터를 종합적으로 활용하게 되는데, 특히, 정확한 사고유형 확인을 위해 절분점 감시 메카니즘이 새롭게 채택되며, 또한, 선로사고시 시스템 운영자들의 오류로부터 발생할 수 있는 파급효과를 최소화하기 위해 고장구간의 자동진단 전략이 개발된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2000.10a
• /
• pp.459-462
• /
• 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 hon 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
• /
• v.49 no.8
• /
• pp.369-373
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.856-858
• /
• 1999

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. 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. This paper describes an algorithm using neural network for pattern recognition and detection of high impedance faults. Wavelet transform analysis gives the time-scale information. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.