Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12495
Title: Evaluation and classification of power quality disturbances based on discrete Wavelet transform and artificial neural networks
Authors: Alshahrani, S
Abbod, M
Alamri, B
Taylor, G
Keywords: Artificial neural networks;Classification;Discrete wavelet transform;Disturbances;Feature extraction;Power quality
Issue Date: 2015
Publisher: IEEE
Citation: Proceedings of the Universities Power Engineering Conference (UPEC 2015), Stoke on Trent, (1-4 September 2015)
Abstract: In this paper, detection method and classification technique of power quality disturbances is presented. Due to the increase of nonlinear load recently, it becomes an essential requirement to insure high level of power supply and efficient commotional consuming. Wavelet Transform represents a powerful mathematical platform which is needed especially at non-stationary situations. Disturbances are fed into wavelets to filter, detect and extract its features at different frequencies. Training of features extracted by DWT is done using artificial neural networks ANN to classify power quality disturbances.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7339928
http://bura.brunel.ac.uk/handle/2438/12495
DOI: http://dx.doi.org/10.1109/UPEC.2015.7339928
ISBN: 9781467396820
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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