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Recognition technology of winding deformation based on principal components of transfer function characteristics and artificial neural network

Recognition technology of winding deformation based on principal components of transfer function characteristics and artificial neural network
Recognition technology of winding deformation based on principal components of transfer function characteristics and artificial neural network
In this paper, an intelligent identification method for winding deformation fault is proposed. The proposed method is composed of principal components of transfer function characteristics and an artificial neural network (ANN). A sequence of simulative deformation faults with different types, locations and extents are set on the winding of a 10kV transformer. The corresponding status transfer function is acquired with a winding deformation test method excited by M-Sequence. Zeros, poles and the variations of the transfer function are considered to be the features of the winding mechanical status. The principal components of feature are extracted and then used as input to a back-propagation ANN for fault recognition. The winding deformation faults are recognized using the ANN that has been trained and tested using the cross validation method. The results show that the classification method has the ability to simultaneously recognize the deformation faults with different types, locations and extents with high accuracy and is suitable for winding deformation diagnosis. The study presents an idea and a path to identify winding mechanical status intelligently though it conducts on a transformer.
3922-3932
Luo, Yongfen
fcaf75bd-ad1d-4f7f-b065-c97aadc86df7
Ye, Jianqu
a675cf93-d6c6-4526-a003-3108d58bfb0e
Gao, Jiaping
f1d9f93e-48e3-4b58-85a1-58aa41824e56
Chen, George
3de45a9c-6c9a-4bcb-90c3-d7e26be21819
Wang, Guoli
364c8ada-b995-4094-9d8d-dd455c711bf1
Liu, Lei
3629cfc7-2869-4860-84e2-246c67fb21ab
Li, Bin
b96a645c-851a-4861-b2da-c531001e51d4
Luo, Yongfen
fcaf75bd-ad1d-4f7f-b065-c97aadc86df7
Ye, Jianqu
a675cf93-d6c6-4526-a003-3108d58bfb0e
Gao, Jiaping
f1d9f93e-48e3-4b58-85a1-58aa41824e56
Chen, George
3de45a9c-6c9a-4bcb-90c3-d7e26be21819
Wang, Guoli
364c8ada-b995-4094-9d8d-dd455c711bf1
Liu, Lei
3629cfc7-2869-4860-84e2-246c67fb21ab
Li, Bin
b96a645c-851a-4861-b2da-c531001e51d4

Luo, Yongfen, Ye, Jianqu, Gao, Jiaping, Chen, George, Wang, Guoli, Liu, Lei and Li, Bin (2017) Recognition technology of winding deformation based on principal components of transfer function characteristics and artificial neural network. IEEE Transactions on Dielectrics & Electrical Insulation, 24 (6), 3922-3932. (doi:10.1109/TDEI.2017.006655).

Record type: Article

Abstract

In this paper, an intelligent identification method for winding deformation fault is proposed. The proposed method is composed of principal components of transfer function characteristics and an artificial neural network (ANN). A sequence of simulative deformation faults with different types, locations and extents are set on the winding of a 10kV transformer. The corresponding status transfer function is acquired with a winding deformation test method excited by M-Sequence. Zeros, poles and the variations of the transfer function are considered to be the features of the winding mechanical status. The principal components of feature are extracted and then used as input to a back-propagation ANN for fault recognition. The winding deformation faults are recognized using the ANN that has been trained and tested using the cross validation method. The results show that the classification method has the ability to simultaneously recognize the deformation faults with different types, locations and extents with high accuracy and is suitable for winding deformation diagnosis. The study presents an idea and a path to identify winding mechanical status intelligently though it conducts on a transformer.

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6655 (Luo) proofread - Accepted Manuscript
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Accepted/In Press date: 29 August 2017
e-pub ahead of print date: 31 December 2017
Published date: December 2017

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Local EPrints ID: 415915
URI: http://eprints.soton.ac.uk/id/eprint/415915
PURE UUID: e457890b-40f9-4a8c-aade-1a6b85a83f72

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Date deposited: 28 Nov 2017 17:31
Last modified: 17 Dec 2019 05:46

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