Online leakage current classification using convolutional neural network long short-term memory for high voltage insulators on web-based service

Abstract

The paper introduced a deep learning algorithm, convolutional neural network Long Short-Term Memory (CNN-LSTM), which has yet to be deeply researched for classifying the leakage/discharge current on the web-based service. The four data models with the leakage current surge have been collected hourly for more than 14 months in Yunlin County, Taiwan, which was seriously affected by the salt-for pollution. The weather data, which are temperature, humidity, dewpoint, wind direction, wind speed, air pressure, rainfall, and solar illuminance, were utilized as input parameters. Consequently, the Convolution Neural networks’ long short-term memory has been developed to predict the leakage current classification for one-hour intervals. Moreover, the hyperparameter optimization will enhance the proposed model's performance and accuracy by varying configurations. The optimized structure of the Convolution neural network Long short-term memory is also compared with other persistent models, the traditional Long Short-term memory and Bidirectional long-short term memory, to evaluate the performance. The simulation results proved that the optimized convolutional neural network long short-term memory is an appropriate neural network to predict the leakage current classification with a maximum improvement of 54.940% and 82.536% category error; 16.184% and 23.287% accuracy; 27.787% and 27.608% precision in training and validating data, respectively.