Next Article in Journal
Hybrid Simulation of Seismic Responses of a Typical Station with a Reinforced Concrete Column
Previous Article in Journal
Coherent Exciton Dynamics in Ensembles of Size-Dispersed CdSe Quantum Dot Dimers Probed via Ultrafast Spectroscopy: A Quantum Computational Study
Open AccessArticle

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1329; https://doi.org/10.3390/app10041329 (registering DOI)
Received: 6 January 2020 / Revised: 17 January 2020 / Accepted: 19 January 2020 / Published: 15 February 2020
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
With the development of Industry 4.0, as a pivotal part of the power system, large-capacity power transformers are requiring fault diagnostic methods with higher intelligence, accuracy and anti-interference ability. Considering the powerful capability for extracting non-linear features and the sensitivity differences to features of deep learning methods, this paper proposes a deep parallel diagnostic method for transformer dissolved gas analysis (DGA). In view of the insufficient and imbalanced dataset of transformers, adaptive synthetic oversampling (ADASYN) was implemented to augment the fault dataset. Then, the newly constructed dataset was normalized and input into the LSTM-based diagnostic framework. Then, the dataset was converted into images as the input of the CNN-based diagnostic framework. At the same time, the problem of still insufficient data was compensated by the introduction of transfer learning technology. Finally, the diagnostic models were trained and tested respectively, and the Dempster–Shafer (DS) evidence theory was introduced to fuse the diagnostic confidence matrices of the two models to achieve deep parallel diagnosis. The results of the proposed deep parallel diagnostic method show that without complex feature extraction, the diagnostic accuracy rate could reach 96.9%. Even when the dataset was superimposed with 3% random noises, the rate only decreased by 0.62%.
Keywords: adaptive synthetic oversampling; convolutional neural networks; Dempster–Shafer evidence theory; dissolved gas analysis; deep parallel diagnosis; long short-term memory; transformer; fault diagnosis adaptive synthetic oversampling; convolutional neural networks; Dempster–Shafer evidence theory; dissolved gas analysis; deep parallel diagnosis; long short-term memory; transformer; fault diagnosis
MDPI and ACS Style

Wu, X.; He, Y.; Duan, J. A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis. Appl. Sci. 2020, 10, 1329.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop