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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| Vydáno v: | International transactions on electrical energy systems Ročník 31; číslo 12 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken
John Wiley & Sons, Inc
01.12.2021
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| ISSN: | 2050-7038, 2050-7038 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Özbay, Harun Efe, Serhat Berat Özer, İlyas |
| Author_xml | – sequence: 1 givenname: İlyas surname: Özer fullname: Özer, İlyas email: iozer@bandirma.edu.tr organization: Bandırma Onyedi Eylül University – sequence: 2 givenname: Serhat Berat surname: Efe fullname: Efe, Serhat Berat organization: Bandırma Onyedi Eylül University – sequence: 3 givenname: Harun surname: Özbay fullname: Özbay, Harun organization: Bandırma Onyedi Eylül University |
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| SubjectTerms | 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 |
| Title | CNN/Bi‐LSTM‐based deep learning algorithm for classification of power quality disturbances by using spectrogram images |
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