基于栈式稀疏自编码器的矿用变压器故障诊断

许倩文; 吉兴全; 张玉振; 李军; 于永进

doi:10.13272/j.issn.1671-251x.2018040092

基于栈式稀疏自编码器的矿用变压器故障诊断

1.
山东科技大学电气与自动化工程学院，山东青岛266590
2.
国网山东省电力公司东营供电公司，山东东营 257000
3.
国网山东省电力公司威海供电公司，山东威海264200

基金项目:

山东省高等学校科技计划项目（J17KA074）

详细信息

中图分类号: TD611
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出版历程
- 刊出日期: 2018-10-09

Fault diagnosis of mind-used transformer based on stacked sparse auto-encoder

1.
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2.
Dongying Power Supply Company, State Grid Shandong Electric Power Company, Dongying 257000, China
3.
Weihai Power Supply Company, State Grid Shandong Electric Power Company, Weihai 264200, China

摘要

摘要: 鉴于将深度学习应用于变压器故障诊断具有良好的故障诊断效果，提出了一种基于栈式稀疏自编码器的矿用变压器故障诊断方法。通过在自编码器隐含层引入稀疏项限制构成稀疏自编码器，再将多个稀疏自编码器进行堆叠形成栈式稀疏自编码器，并以Softmax分类器作为输出层，建立矿用变压器故障诊断模型；利用大量无标签样本对模型进行无监督预训练，并通过有监督微调优化模型参数。算例分析结果表明，与栈式自编码器相比，栈式稀疏自编码器应用于矿用变压器故障诊断具有更高的准确率。
- 矿用变压器 /
- 故障诊断 /
- 深度学习 /
- 栈式稀疏自编码器 /
- Softmax分类器
Abstract: In view of application of deep learning to transformer fault diagnosis had a good fault diagnosis effect, a fault diagnosis method of mind-used transformer based on stacked sparse auto-encoder was proposed. Sparse auto-encoder is constructed by introducing sparse item constraint in hidden layer of auto-encoder, then the multiple sparse auto-encoders are stacked to form stacked sparse auto-encoder, and Softmax classifier is used as output layer to establish mine-used transformer fault diagnosis model. A large number of unlabeled samples are used to carry out unsupervised pre-training for the model, and the model parameters are optimized through supervised fine-tuning. The example analysis results show that stacked sparse auto-encoder is more accurate than stack auto-encoder in application of fault diagnosis of mind-used transformer.
- mind-used transformer /
- fault diagnosis /
- deep learning /
- stacked sparse auto-encoder /
- Softmax classifier