A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network
The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and paramet...
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Online Access: | http://dx.doi.org/10.1155/2021/6656061 |
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doaj-97e8f738105d44fda2eaf7d5dd20b1d02021-04-05T00:01:16ZengHindawi LimitedMathematical Problems in Engineering1563-51472021-01-01202110.1155/2021/6656061A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural NetworkQian Wang0Shinan Wang1Rong Shi2Yong Li3School of Automation and Information EngineeringSchool of Automation and Information EngineeringState Grid Shaanxi Electric Power Company Economic Research InstituteTrinity International Ltd.The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis.http://dx.doi.org/10.1155/2021/6656061 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qian Wang Shinan Wang Rong Shi Yong Li |
spellingShingle |
Qian Wang Shinan Wang Rong Shi Yong Li A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network Mathematical Problems in Engineering |
author_facet |
Qian Wang Shinan Wang Rong Shi Yong Li |
author_sort |
Qian Wang |
title |
A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network |
title_short |
A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network |
title_full |
A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network |
title_fullStr |
A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network |
title_full_unstemmed |
A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network |
title_sort |
power transformer fault diagnosis method based on random vector functional-link neural network |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1563-5147 |
publishDate |
2021-01-01 |
description |
The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis. |
url |
http://dx.doi.org/10.1155/2021/6656061 |
work_keys_str_mv |
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