Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network

Dasgupta, Aritra; Nath, Sudipta; Das, Arabinda

doi:10.1080/15325008.2012.716495

Cited by 84 publications

(37 citation statements)

References 19 publications

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“…The wavelet transform has capabilities of providing accurate location and classification of electrical transients in power system as depicted by Dasgupta et al [6]. Continuous wavelet transforms (CWT) for a given function f(t) can be calculated by (1) CWTðf ; a; bÞ…”

Section: Proposed Combined Wavelet and Ann Based Fault Section Identimentioning

confidence: 99%

“…ANN, Fuzzy logic and hybrid techniques using combined wavelet, SVM, ANN and/or fuzzy logic has been applied for protection of transmission lines by Reddy and Mohanta [19], Al-Shaher et al [3], Abdollahi et al [1], Gayatri and Kumarappan [10], Kalam et al [16], Yusuff et al [25], Dasgupta et al [6], Saravanan et al [21], Ekici [8] and Wang et al [24]. But these techniques are non-directional.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Yadav

Swetapadma

2015

International Journal of Electrical Power & Energy Systems

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Section: Proposed Combined Wavelet and Ann Based Fault Section Identimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Yadav

Swetapadma

2015

International Journal of Electrical Power & Energy Systems

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“…BPNN algorithm is constructed for decision making once the fault is investigated using DWT. Aritra Dasgupta et al [31] have proposed a novel scheme for transmission line faults classification and location using wavelet entropy and neural network. The authors used Db4 mother wavelet for analyzing faulty voltage signals and the entropies of the wavelet decompositions have been fed to neural network for fault classification and location.…”

Section: B Wavelet With Artificial Neural Networkmentioning

confidence: 99%

Intelligent techniques for fault diagnosis in transmission lines — An overview

Singh

Vishwakarma

2015

2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)

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This paper presents survey and review of research and development in the field of faults detection, classification and their location that occurs in the transmission network. Transmission lines are integral part of the power system network and its main aim is to transmit the generated power to the consumer with least interruption. With an ever-increasing demand of electric power day by day because of increasing industrialization and urbanization of life style, fast and accurate fault analysis is very essential for better performance and minimal interruptions in power system. Fast detection and clearing of faults in transmission lines is very important for maintaining the normal operation of power system network. This paper presents a comprehensive review of various algorithms that have been developed and used over the last few years for the detection and classification of faults in transmission lines and mainly concentrate on following algorithm and their implementation for fault analysis such as, wavelet transform (WT), discrete wavelet transform (DWT), multi-resolution analysis (MRA), Wavelet energy entropy, artificial neural network (ANN), wavelet-neuro-fuzzy approach and support vector machine (SVM). Keywords-Wavelet transform (WT), Discrete wavelet transform (DWT), Artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM).I.

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“…The selection of proper mother wavelet with a closely matches with the required clues for classifier would result to a better performance of the protection scheme. The mother wavelet applied for this study is Daubechies 4 (db4), as it indicated by the researchers to be a most suitable mother wavelet to give enough information for classifier [4,6,7].…”

Section: B Features Extractionmentioning

confidence: 99%

“…The effective protection system is the most concern matter in order to help in fast maintenance and restoration of power energy supply resulting in improving economy and system availability, reducing cost maintenance, and shortening the repair duration [1]. A number of literatures have been proposed for fault classification techniques in power systems [2][3][4]. Generally, fault classification techniques consist of two stages.…”

Section: I Introductionmentioning

confidence: 99%

Selection of suitable features for fault classification in transmission line

Mahmud

Ibrahim

Osman

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Power system protective relaying is an important feature for efficient and reliable power flow operation. The traditional fault classification scheme using the steady state component is easily affected by environment factors. Then, it is important to provide power protection scheme that offers a better classification performance based on fault-generated transient signals where it is immune to the surrounding factors. This paper search for important features for fault classification in transmission lines using Wavelet Transform (WT) and multilayered perceptron (MLP) network. Six (6) features namely wavelet energy, mean, standard deviation, entropy, kurtosis and skewness are obtained from the WT. For analyzing these features, a MLP network trained by Levenberg-Marquardt (LM) algorithm is used as classifier to identify the fault types. The classification accuracy is evaluated using three types of dataset conditions; ideal dataset (no noise involvement), dataset with Signal-to-noise ratio (SNR) of 30 (30 dB noise) and dataset with SNR of 20 (20 dB noise). Simulation results show that combination of the wavelet mean and standard deviation shows the highest performance accuracy for all conditions tested.

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Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network

Cited by 84 publications

References 19 publications

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Intelligent techniques for fault diagnosis in transmission lines — An overview

Selection of suitable features for fault classification in transmission line

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Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network

Cited by 84 publications

References 19 publications

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Intelligent techniques for fault diagnosis in transmission lines &#x2014; An overview

Selection of suitable features for fault classification in transmission line

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Intelligent techniques for fault diagnosis in transmission lines — An overview