Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection
Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2019/1019845 |
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doaj-aefe65182a6946b98c9ab2d8b1b232222020-11-25T02:36:02ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/10198451019845Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series ConnectionChun Yan0Meixuan Li1Wei Liu2College of Mathematics and System Science Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mathematics and System Science Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering Shandong University of Science and Technology, Qingdao 266590, ChinaDissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.http://dx.doi.org/10.1155/2019/1019845 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chun Yan Meixuan Li Wei Liu |
spellingShingle |
Chun Yan Meixuan Li Wei Liu Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection Mathematical Problems in Engineering |
author_facet |
Chun Yan Meixuan Li Wei Liu |
author_sort |
Chun Yan |
title |
Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection |
title_short |
Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection |
title_full |
Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection |
title_fullStr |
Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection |
title_full_unstemmed |
Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection |
title_sort |
transformer fault diagnosis based on bp-adaboost and pnn series connection |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2019-01-01 |
description |
Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost. |
url |
http://dx.doi.org/10.1155/2019/1019845 |
work_keys_str_mv |
AT chunyan transformerfaultdiagnosisbasedonbpadaboostandpnnseriesconnection AT meixuanli transformerfaultdiagnosisbasedonbpadaboostandpnnseriesconnection AT weiliu transformerfaultdiagnosisbasedonbpadaboostandpnnseriesconnection |
_version_ |
1724801779948847104 |