Open AccessResearch on Transformer Condition Recognition Based on Acoustic Signal and One-dimensional Convolutional Neural Networks
Author(s) -
Yue Ma,
Xin Wang,
Wenjin Zhou,
Xiang Li,
Juan Martino,
Zhuo Zheng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012078
Subject(s) - fast fourier transform , convolutional neural network , transformer , computer science , pattern recognition (psychology) , artificial neural network , artificial intelligence , speech recognition , algorithm , engineering , voltage , electrical engineering
To solve the problem of transformer condition recognition, this paper propose a transformer condition recognition method based on acoustic signal and One-dimensional Convolutional Neural Networks(1D-CNN). In order to verify the effectiveness of 1D-CNN algorithm in the field of transformer condition recognition, a platform for acquisition of acoustic signals from a 500 kV transformer is built to carry out the acoustic signal acquisition test for four conditions of the transformer. The acoustic signal data sets are also made, and the 1D-CNN algorithm is used to calculate the recognition accuracy of the transformer conditions. According to test results, 1D-CNN algorithm, as a new structure of deep-learning algorithm, can properly classify acoustic signals of the transformer, and its classification accuracy is higher than those of FFT-BP, SVM, FFT-SAE and other algorithms. In order to explore the internal mechanism of 1D-CNN algorithm, in this paper, a t-SNE visual analysis is also conducted to reveal the performance of 1D-CNN algorithm.