Novel technique for fault location estimation on parallel transmission lines using wavelet

Jung, Hosung; Park, Young; Han, Moonseob; Lee, Changmu; Park, Hyunjune; Shin, Min Jae

doi:10.1016/j.ijepes.2006.05.002

Cited by 37 publications

(11 citation statements)

References 6 publications

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“…Fig 6 shows the architecture of a PNN network. Here a2r1 is itself a column matrix, comprising 28 columns representing the level 2 approximate entropies of the pure signal as well as the ten different types of faults in the "red" phase of each of the 3 lines.Similarly d2r1 is also a column matrix comprising 28 columns representing the level 2 detail entropy values of the pure signal and the ten other types of fault signals in the "red" phase of each of the three lines .Each of the matrices INP1,INP2,INP3,INP4,INP5 and INP6 are of the order 9*28.So the input matrix P is a 9*(28*6)=9*168 matrix.The targets are 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27 …”

Section: Wavelet Transform Based Feature Extractionmentioning

confidence: 99%

Fault detection on a ring-main type power system network using artificial neural network and wavelet entropy method

Bhattacharya

2015

International Conference on Computing, Communication &Amp; Automation

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This paper intends to present an approach to classify different types of faults and to identify the location of the faults in a non-radial power system network using ElectroMagnetic Transients Program(ATP/EMTP)software and Artificial Neural Network(ANN).Firstly, a balanced threephase system is designed with a RLC load and then different types of faults(single line to ground fault, line to line fault, double line to ground fault and three phase fault) are created at various points of the transmission line. The transmission line model is simulated using the EMTP software. The resulting current waveforms under different fault conditions are observed from the sending end. Fault occurs at the point where peaks appear in the current signal when viewed from the sending end. These current waveforms are analyzed using the wavelet toolbox in MATLAB and the entropy values of the current signals, so obtained, are given as input to the artificial neural network for automatic fault classification and identification of the faulty line. This scheme is also tested under different types of faults with different fault resistances and varying fault locations and the results show that it is able to discriminate the faults and identify the fault locations rapidly and correctly.

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Section: Wavelet Transform Based Feature Extractionmentioning

confidence: 99%

Fault detection on a ring-main type power system network using artificial neural network and wavelet entropy method

Bhattacharya

2015

International Conference on Computing, Communication &Amp; Automation

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“…The Daubechies family is one of the most suitable wavelet families in analyzing power-system transients [12,16,[19][20][21][22][23]. Although there are no definite criteria for the selection of wavelets, the best choice is a wavelet that most strikingly exhibits the phenomena to be studied.…”

Section: Power System Model and Detailsmentioning

confidence: 99%

Fault Classification Scheme Based on the Adaptive Resonance Theory Neural Network for Protection of Transmission Lines

Upendar

Gupta

Singh

2010

Electric Power Components and Systems

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This article presents a new approach to classify various types of power system faults using wavelet transforms and the adaptive resonance theory. The key idea underlying the approach is to decompose a given disturbance signal into other signals, which represents a smoothed and detailed version of the original signal. The proposed technique consists of a preprocessing unit based on discrete wavelet transform in combination with adaptive resonance theory. Through wavelet analysis, faults are decomposed into a series of wavelet components, each of which is a timedomain signal that covers a specific octave frequency band. The parameters selected for fault classification are the detailed coefficients of all phase current signals that are collected only at the sending end of a transmission line. The information is then fed into adaptive resonance theory for classifying the faults. The study is performed on a sample power system network. Extensive simulation studies carried out using MATLAB (http://www.mathworks.com) shows that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions, but it is also reliable, fast, and computationally efficient tool.

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“…Many diagnostic approaches have been proposed in the literature, including fault location based on the amount of electricity, which includes one-terminal method [2], [3], two-terminal method [4], [5], traveling wave analysis method [6], [7], resistance measurement method [8], and the determination of fault location estimation using wavelet transform [9]. This paper proposed discrete wavelet transformation using the Clarke's transformation to determine the fault location estimation and classification on the parallel transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

Saini

Zin

Mustafa

et al. 2018

IJECE

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This paper proposed a new algorithm for fault location and classification using wavelet based on Clarke's transformation to obtain the fault current. This novel method of fault current approach is studied by comparing the use of the glide path of the fault voltage. The current alpha and beta (Current Mode) were used to transform the signal using discrete wavelet transform (DWT). The fault location was determined by using the Clarke's transformation, and then turned into a wavelet, which was very precise and thorough. The most accurate was the mother wavelet Db4 which had the fastest time and smallest error detection when compared with the other wavelet mothers. In this study, the Clarke's transformation is also compared with the Karenbauer's, which has produced results with similar error percentage. The simulation results using PSCAD / EMTDC software showed that the proposed algorithm could distinguish internal and external faults to get the current signal in the transformation of a signal fault.

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Novel technique for fault location estimation on parallel transmission lines using wavelet

Cited by 37 publications

References 6 publications

Fault detection on a ring-main type power system network using artificial neural network and wavelet entropy method

Fault detection on a ring-main type power system network using artificial neural network and wavelet entropy method

Fault Classification Scheme Based on the Adaptive Resonance Theory Neural Network for Protection of Transmission Lines

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

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