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|Title:||Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids|
|Keywords:||Power quality;Events;Feature extraction;Wavelet transform;Classification;Artificial neural networks|
|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.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
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