Open AccessMethod of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm
Author(s) -
Duan Lian,
Hu Jun,
Zhao Gen,
Chen Kunjin,
Wang Shan X.,
He Jinliang
Publication year - 2019
Publication title -
high voltage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 20
ISSN - 2397-7264
DOI - 10.1049/hve.2019.0067
Subject(s) - computer science , waveform , encoder , artificial intelligence , pattern recognition (psychology) , algorithm , softmax function , time domain , frequency domain , deep learning , computer vision , telecommunications , radar , operating system
In this study, an inter‐turn fault diagnosis method is proposed based on deep learning algorithm. 12‐channel data is obtained in MATLAB/Simulink as the time‐domain monitoring signals and labelled with 16 different fault tags, including both primary and secondary voltage and current waveforms. An auto‐encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms. The overall waveforms compose a two‐dimension data matrix and the auto‐encoder is trained to extract the features in the multi‐channel waveforms. The selected features are convoluted with the original data, generating a one‐dimensional vector as the input to the softmax classifier. Variables such as type, activation function and depth of auto‐encoder, sparsity of sparse auto‐encoder, number of features and pooling strategies are studied, which gives an intuitive process to train a proper learning model. The overall recognition accuracy reaches 99.5%. Signal characteristics such as channel selection, time span of the input signal and signal sampling frequency are studied to find the best solution for the inter‐turn fault detection of the three‐phase transformer. The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis, indicating its potential and prospect in the next‐generation smart transformers.