CNN/Bi‐LSTM‐based deep learning algorithm for classification of power quality disturbances by using spectrogram images

This paper, using an inverse signal approach, presents a novel deep learning algorithm based on a convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) together with spectrograms for the classification of power quality disturbances (PQDs). The proposed method focuses...

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Bibliographic Details

Published in:	International transactions on electrical energy systems Vol. 31; no. 12
Main Authors:	Özer, İlyas, Efe, Serhat Berat, Özbay, Harun
Format:	Journal Article
Language:	English
Published:	Hoboken John Wiley & Sons, Inc 01.12.2021
Subjects:	Algorithms Artificial neural networks bi‐LSTM Classification convolutional neural network Deep learning Disturbances energy quality Image classification Image quality Machine learning power system analysis Sine waves spectrogram Spectrograms
ISSN:	2050-7038, 2050-7038
Online Access:	Get full text
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