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

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.

Research on Blended Teaching of Flipped Classroom Based on CNN-SSA-Bi-LSTM Deep Learning Model Computer Media

“…Finally, an ultra-short-term prediction model based on CNN-Bi-LSTM is established [ 28 ], whose input X is 4 variables, the input step size of each variable is 6, and the output y is the predicted value in the next 45 minutes, which is defined as follows: …”

LED Aydınlatma Sistemlerinde Derin Öğrenme Tabanlı Harmonik Tahminleme

“…Bu kapsamda dizi verilerini işlemek için tekrarlayan sinir ağları (RNN'ler) geliştirilmiştir. Klasik ileri beslemeli YSA'lar, yalnızca girdi olarak mevcut örnekleri dikkate alsa da, RNN'ler, mevcut örneklerin yanı sıra zaman içinde algıladıklarını da girdi olarak uygular [12]. Bu özellikleri sayesinde RNN'ler tahminleme çalışmalarında sıklıkla kullanılmaya başlanmıştır [13].…”

Power quality disturbance classification method based on multimodal parallel feature extraction network

“…The method represented by Long short-term memory (LSTM) automatically extracts temporal features from PQD signals and then performs classification [9]. Combining CNN and LSTM, CNN is used to extract spatial features first, and then LSTM is used to extract temporal features from the reduced-dimensional feature map [10]. This hybrid model provides better classification results than a single model, but the method causes partial loss of temporal features during image transformation.…”