Convolutional neural network bidirectional long short-term memory to online classify the distribution insulator leakage currents

Abstract

In this project, the online monitoring systems to classify the leakage current of 15 kV and 25 kV distribution insulators were installed in mountain areas in Taiwan. The convolution neural network bidirectional Long Short-Term Memory (CNN-Bi-LSTM) was employed to categorize the leakage current levels based on the field experiences of the TaiPower company. The sequential weather factors and the different distribution insulators leakage current were collected every hour and utilized in the proposed model's learning process. In addition, a simple grid search parameter optimization was implemented to identify the optimum architecture of CNN-Bi-LSTM for each data collection. Moreover, the optimized CNN-Bi-LSTM structure performances were evaluated and compared with the gated recurrent unit (GRU), LSTM, CNN-LSTM, and Bi-LSTM neural networks. The experiments results proved that the proposed CNN-Bi-LSTM improved the category cross-entropy error, the accuracy, and the precision metric with a maximum enhancement of 71.331%, 14.382%, and 14.042% in the training data and 91.250%, 17.529%, and 17.343% in the validating data, respectively. Moreover, the experiment outcomes in different data models prove that the 24-sequential weather parameters have a significant impact on the achievement of CNN-Bi-LSTM in categorizing the leakage current. The optimized CNN-Bi-LSTM structures were applied in the online monitoring systems to provide a better schedule for maintenance operations at the Taipower company.