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Method of inter-turn fault detection for next-generation smart transformers based on deep learning algorithm

TitleMethod of inter-turn fault detection for next-generation smart transformers based on deep learning algorithm
Publication TypeJournal Article
Year of Publication2019
AuthorsDuan L, Hu J, Zhao G, Chen K, Wang SX, He J
JournalHIGH VOLTAGE
Volume4
Pagination282-291
Date PublishedDEC
Type of ArticleArticle
ISSN2397-7264
Keywords12-channel data, channel selection, comprehensive fault waveforms, current waveforms, deep learning algorithm, deep learning framework, fault diagnosis, fault tags, fault type, fault waveforms, Feature extraction, feature selection, input signal, inter-turn fault detection, inter-turn fault diagnosis method, learning (artificial intelligence), learning model, MATLAB, multichannel waveforms, next-generation smart transformers, power engineering computing, power transformers, secondary voltage, signal classification, signal sampling, signal sampling frequency, Simulink, sparse auto-encoder, time-domain monitoring signals, transformer fault diagnosis, two-dimension data matrix
Abstract

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.

DOI10.1049/hve.2019.0067