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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Vydané v:International transactions on electrical energy systems Ročník 31; číslo 12
Hlavní autori: Özer, İlyas, Efe, Serhat Berat, Özbay, Harun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken John Wiley & Sons, Inc 01.12.2021
Predmet:
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
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Popis
Shrnutí: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 on the region where the PQD event occurs, using a deep learning‐based spectrogram with the aim of increasing classification success rates. In the proposed approach, the time shift of the signal relative to the pure sine signal was found and relative to this time shift, an inverse sine wave was generated according to the original signal. By collecting this sine wave together with the original signal, spectrograms of both signals were obtained and converted into red/green/blue (RGB) images that were then combined. Finally, classification was carried out via CNN/Bi‐LSTM. In this context, 29 different disturbance events in both single and combined structures were used. The proposed model was applied to the disturbance events and 99.33% classification accuracy was obtained. The proposed method focuses on the region where the PQD event occurs, using a deep learning‐based spectrogram with the aim of increasing classification success rates. In the proposed approach, the time shift of the signal relative to the pure sine signal was found and relative to this time shift, an inverse sine wave was generated according to the original signal.
Bibliografia:Funding information
Handling Editor
Wu Xuan
Scientific Research Project (BAP) Coordinatorship of Bandırma Onyedi Eylül University, Grant/Award Number: BAP‐19‐1003‐006
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ISSN:2050-7038
2050-7038
DOI:10.1002/2050-7038.13204