Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12818
Title: Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids
Authors: Alshahrani, S
Abbod, M
Alamri, B
Keywords: Power quality;Events;Feature extraction;Wavelet transform;Classification;Artificial neural networks
Issue Date: 2015
Publisher: IEEE
Citation: 2015 Saudi Arabia Smart Grid (SASG), Jeddah, (7- 9 December 2015)
Abstract: In this paper, A powerful signal processing method wavelet transform is presented to detect power quality events among one of the Artificial intelligence techniques which is Artificial neural networks as a classification system. As a result of the increased applications of non-linear load, it becomes important to find accurate detecting method. Wavelet Transform represents an efficient signal processing algorithm for power quality problems especially at non-stationary situations. These events are generated and filtered using wavelet as well as extraction of their features at different frequencies. Thereafter, a training process is done using ANN to classify power quality events.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7449296
http://bura.brunel.ac.uk/handle/2438/12818
DOI: http://dx.doi.org/10.1109/SASG.2015.7449296
ISBN: 9781467394543
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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