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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/16972
Title: ANN based power system fault classification
Authors: Upendar J.
Gupta, Chandra Prakash
Singh, Girish Kumar
Published in: Proceedings of IEEE Region 10 Annual International Conference, TENCON
Abstract: This paper presents Wavelet based back propagation algorithm for classifying the power system faults, which is quite reliable, fast and computationally efficient. The proposed technique consists of a preprocessing unit based on discrete wavelet transform (DWT) in combination with an artificial neural network (ANN). The DWT acts as extractor of distinctive features in the input current signal which are collected at source end. The information is then fed into ANN for classifying faults. It can be used on-line following the operation of digital relays or off-line using the data stored in the digital recording apparatus. Extensive simulation studies carried out using MATLAB show that the proposed algorithm provides an accepted degree of accuracy in fault classification under different fault conditions.
Citation: Proceedings of IEEE Region 10 Annual International Conference, TENCON, (2008). Hyderabad
URI: https://doi.org/10.1109/TENCON.2008.4766623
http://repository.iitr.ac.in/handle/123456789/16972
Issue Date: 2008
Keywords: Artificial neural network
Fault classification
Wavelet transform
ISBN: 1424424089; 9781424424085
Author Scopus IDs: 26423373000
7202352469
57193351909
Author Affiliations: Upendar, J., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India
Gupta, C.P., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India
Singh, G.K., Department of Elec
Corresponding Author: Upendar, J.; Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India; email: jallaupendar@gmail.com
Appears in Collections:Conference Publications [EE]

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