Please use this identifier to cite or link to this item:
|Title:||Power Quality Event Classification Using Transfer Learning on Images|
|Citation:||IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2019, 2019, Vol., , pp.-|
|Abstract:||Given the ever-increasing complexity of the electrical grid system, power quality events have been surging in frequency with each passing day. Due to their potential to cause massive losses for a wide variety of customers, it is crucial that such events are detected and classified immediately for appropriate response. in this paper, a novel approach has been developed wherein Transfer Learning techniques have been employed to classify power quality events using image classification. More specifically, the VGG16 model has been utilized to classify five distinct power quality issues by using scalograms as input images. 489 scalograms were generated via feature extraction using wavelet transforms. The VGG16 model has then been trained and tested using the same. Thereafter, the model performance has been evaluated, and the results have been discussed. � 2019 IEEE.|
|Appears in Collections:||2. Conference Papers|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.