Real-Time Leakage Current Classification of 15kV and 25kV Distribution Insulators Based on Bidirectional Long Short-Term Memory Networks With Deep Learning Machine

Abstract

This paper presents an online monitoring system to classify the leakage/discharge current of the insulator in coastal sites using Bidirectional Long short-term memory (Bi-LSTM) models on a web-based service. The remote monitoring methodology uses environmental parameters to classify the peak level of leakage/discharge current of the 15kV and 25kV distribution insulators. The sequential weather data, the humidity, temperature, rainfall, dew point, solar illumination, wind speed, air pressure, and wind direction are automatically collected hourly in real-time and transferred to data servers. The hyperparameter optimization for the structure of Bi-LSTM is utilized through the grid search capability in a deep learning machine. The optimized design of Bi-LSTMs improves the performance and accuracy in predicting the leakage/discharge current classification for HDPE and SR of 15kV and 25kV insulators. Compared with persistent methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU), the Bi-LSTM has better performance and higher accuracy in predicting the leakage/discharge levels, which are utilized to evaluate the surface pollution of insulators. The optimized structure of the proposed Bi-LSTM model could achieve a maximum improvement of 49.529% error, 12.761% accuracy, 72.736% error, and 36.641% accuracy for training and validating data compared with other models. Moreover, a web-based service is developed for maintenance staff to interact with all current and predicted status of insulators. This online leakage current classification has been installed in Mailiao Township in Taiwan and could establish a reasonable maintenance mechanism for 15kV and 25kV distribution insulators.