Please use this identifier to cite or link to this item:
http://bura.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 Electrical Engineering Research Papers |
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Fulltext.docx | 487.14 kB | Unknown | View/Open |
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