Open AccessFault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique
Author(s) -
Frizzo Stefe Stéfano,
Zanetti Freire Roberto,
Henrique Meyer Luiz,
Picolotto Corso Marcelo,
Sartori Andreza,
Nied Ademir,
Rodrigues Klaar Anne Carolina,
Yow KinChoong
Publication year - 2020
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2020.0083
Subject(s) - computer science , wavelet , autoregressive model , feature extraction , artificial intelligence , detector , artificial neural network , insulator (electricity) , deep learning , wavelet transform , mean squared error , fault (geology) , time series , pattern recognition (psychology) , algorithm , machine learning , engineering , mathematics , statistics , telecommunications , seismology , geology , electrical engineering
Identifying problems in insulators is a task that requires the experience of the operator. Contaminated insulators generally do not represent a system failure, however, due to higher surface conductivity, they may suffer from electrical discharges and may result in irreversible failures. The identification of possible failures in inspections can help to forecast faults to improve reliability in the power grid. Based on this need, this article presents a study on fault prediction in distribution insulators, through a laboratory evaluation in a contaminated insulator, where 13.8 kV (root mean square) was applied considering an ultrasound detector connected to a computer for data set acquisition. In the sequence, a time series prediction, using a hybrid deep learning technique defined as wavelet long short‐term memory (LSTM), was performed. The hybrid LSTM was proposed considering feature extraction through the wavelet energy coefficient. Finally, for a complete evaluation, deeper LSTM layers were included, and both the training method and the hardware configuration were evaluated. The wavelet LSTM algorithm showed interesting accuracy results when compared to classic prediction algorithms like the non‐linear autoregressive exogenous model.