Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network

Hasabe, Ramchandra P.; Vaidya, A. P.

doi:10.12720/sgce.3.3.283-290

Cited by 6 publications

(9 citation statements)

References 6 publications

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“…The system consists of three generators of 330kV each located on either end of the transmission line. The transmission line has been modelled using distributed parameters [4] so that it more accurately describes a very long transmission line.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

Aneke

Ezechukwu

Tagboh³

2019

JERR

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This paper proposes a fault (line-to-line) location on Ikeja West – Benin 330kV electric power transmission lines using wavelet multi-resolution analysis and neural networks pattern recognition abilities. Three-phase line-to-line current and voltage waveforms measured during the occurrence of a fault in the power transmission-line were pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high-frequency details and low-frequency approximations. The patterns formed based on high-frequency signal components were arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low-frequency approximations were arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The new method uses both low and high-frequency information of the fault signal to achieve an exact location of the fault. The neural network was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was carried using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrate that neural network pattern recognition and wavelet multi-resolution analysis approach are efficient in identifying and locating faults on transmission lines as the average percentage error in fault location was just 0.1386%. This showed that satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.

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Section: Methodsmentioning

confidence: 99%

Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

Aneke

Ezechukwu

Tagboh³

2019

JERR

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“…There are many applications included in WTs that had been developed to detect and classify transmission line faults. The fault identification for two terminal transmission lines has acquired worthy interest through previous studies [7][8][9][10][11][12][13][14][15][16][17][18][19]. The current and voltage signals were utilised through previous algorithms to detect and classify faults on protected transmission lines.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on single-end measuring systems, several methods have high speed and less impact on external phenomena such as noise [11][12][13][14][15][16][17][18][19]. The method in [11] addressed a fault identification technique by DWT and spectral energy analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fault identification techniques that were dependent on artificial intelligent methods have already been introduced in [17][18][19]. In spite of the fact that the artificial neural network (ANN)based methods have successfully completed in deciding the proper fault type, the main drawback of an ANN is that it requires a huge amount of training.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Sensitive/stable complementary fault identification scheme for overhead transmission lines

Zaki

El‐Sehiemy

Amer

et al. 2019

IET Generation, Transmission & Distribution

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“…Yusuff et al [7] developed a fault location methods use the combination of stationary wavelet transform, determinant function feature, and support vector machine at one or both ends of a transmission line to determine where a fault has occurred. Hasabe et al [8] presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission lines. Moreover, Chen et al [9] introduced a stationary wavelet framework for the damage detection on sudden stiffness reduction of building structures.…”

Section: Introductionmentioning

confidence: 99%

Detecting Abrupt Changes in a Noisy van der Pol Type Oscillator

Nema

Kowalczyk

2015

Differ Equ Dyn Syst

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Many signals produced by dynamical systems may undergo abrupt changes such as a jump or a sharp change. Detecting such change points is of primary importance in many applications ranging from exploratory data analysis to diagnosis. This paper addresses the detection of abrupt changes in a noisy van der Pol oscillator as a model of an electrical circuit with nonlinear resistance. The proposed approach combines wavelet analysis with information entropy in order to extract signal frequencies corresponding to any abrupt changes that occur. We also investigate the influence of noise intensity on detecting change points in the model system. Performance is evaluated on simulated data generated by using different model parameters.

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Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network

Cited by 6 publications

References 6 publications

Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

Sensitive/stable complementary fault identification scheme for overhead transmission lines

Detecting Abrupt Changes in a Noisy van der Pol Type Oscillator

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