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 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.

Automated summary

This paper introduces a novel deep learning algorithm based on CNN and Bi-LSTM for classifying power quality disturbances using an inverse signal approach, achieving a high classification accuracy of 99.33% through spectrograms and RGB images.