Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

A Novel Approach to Transformer Fault Diagnosis Based on Transfer Learning

Author(s): Su Chao, Bai Hao* and Chen Wenquan

Volume 15, Issue 1, 2022

Published on: 31 March, 2022

Page: [41 - 50] Pages: 10

DOI: 10.2174/2352096515666220221103939

Price: $65

conference banner
Abstract

Background: The condition of the power transformer directly affects the reliability and efficiency of the power system. The dissolved gas analysis (DGA) has been widely recognized as one of the effective methods in the field of transformer fault diagnosis.

Objective: To tackle the problem of insufficient single transformer fault data and weak generalization ability of the diagnosis model, this paper proposes a transformer fault diagnosis model based on data cleaning and transfer learning.

Methods: 21 kinds of dissolved gas characteristics of the to-be-Diagnosed Transformer (TDT) and Auxiliary Transformers (ATs) are selected as fault features to detect transformer fault. The first data cleaning is used for Auxiliary Fault Data (AFD) based on similarity analysis between Target Fault Data (TFD) and AFD. Then the TFD and AFD are all cleaned to remove the singular edge interference data for the second cleaning. The transfer learning algorithm is applied to extract effective information from AFD and train the fault diagnosis model.

Results: Test results show that the proposed method can improve the efficiency of fault diagnosis and the accuracy of fault identification.

Conclusion: The two data cleanings complement each other, and both play a role in eliminating bad data and ensuring the accuracy of the fault diagnosis. Transfer learning can effectively extract effective information from AFD and train a better transformer fault diagnotor to improve fault diagnosis accuracy.

Keywords: Data cleaning, fault diagnosis, transformer fault, transfer learning, TrAdaBoost algorithm, dissolved gas analysis.

« Previous Next »
Graphical Abstract

[1]
R.H. Ahmed, "M.M. YAACOB, and M. Abdulrazzaq, “Faults diagnosis and assessment of transformer insulation oil quality: Intelligent methods based on dissolved gas analysis A-review", IACSIT Int. J. Eng. Technol., vol. 4, pp. 54-60, 2015.
[http://dx.doi.org/10.14419/ijet.v4i1.3941]
[2]
N. Lelekakis, D. Martin, W. Guo, and J. Wijaya, "Comparison of dissolved gas-in-oil analysis methods using a dissolved gas-in-oil standard", IEEE Elec. Insul. Mag., vol. 27, pp. 29-35, 2011.
[http://dx.doi.org/10.1109/MEI.2011.6025366]
[3]
Y. Zhang, "A new support vector machine model based on improved imperialist competitive algorithm for fault diagnosis of oil- immersed transformers", J. Electr. Eng. Technol., vol. 12, pp. 830-839, 2017.
[http://dx.doi.org/10.5370/JEET.2017.12.2.830]
[4]
H. Malik, and S. Mishra, ""Extreme learning machine based fault diagnosis of power transformer using IEC TC10 and its related data", In:", Annual IEEE India Conference (INDICON), pp. 2015, India, pp. 1-5-5, .
[http://dx.doi.org/10.1109/INDICON.2015.7443245]
[5]
X. Huang, X. Wang, and Y. Tian, Research on transformer fault diag- nosis method based on GWO optimized hybrid kernel extreme learning machine.In: 2018 Condition Monitoring and Diagnosis., IEEE: Perth, WA, Australia, 2018, pp. 1-5.
[http://dx.doi.org/10.1109/CMD.2018.8535862]
[6]
H. Wu, X. Li, and D. Wu, "RMP neural network based dissolved gas analyzer for fault diagnostic of oil-filled electrical equipment", IEEE Trans. Dielectr. Electr. Insul., vol. 18, pp. 495-498, 2011.
[http://dx.doi.org/10.1109/TDEI.2011.5739454]
[7]
J-H. Yi, "Improved probabilistic neural networks with self- adaptive strategies for transformer fault diagnosis problem", Adv. Mech. Eng., vol. 8, pp. 1-13, 2016.
[http://dx.doi.org/10.1177/1687814015624832]
[8]
J. Dai, H. Song, G. Sheng, and X. Jiang, "Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network", IEEE Trans. Dielectr. Electr. Insul., vol. 24, pp. 2828-2835, 2017.
[http://dx.doi.org/10.1109/TDEI.2017.006727]
[9]
T. Kari, "An integrated method of ANFIS and Dempster-Shafer theory for fault diagnosis of power transformer", IEEE Trans. Dielectr. Electr. Insul., vol. 25, no. 1, pp. 360-371, 2018.
[http://dx.doi.org/10.1109/TDEI.2018.006746]
[10]
C. Zhang, Y. He, S. Jiang, T. Wang, L. Yuan, and B. Li, "Transformer fault diagnosis method based on self-powered RFID sensor tag, DBN, and MKSVM", IEEE Sens. J., vol. 19, pp. 8202-8214, 2019.
[http://dx.doi.org/10.1109/JSEN.2019.2919868]
[11]
J. Li, Q. Zhang, K. Wang, J. Wang, T. Zhou, and Y. Zhang, "Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine", IEEE Trans. Dielectr. Electr. Insul., vol. 23, no. 2, pp. 1198-1206, 2016.
[http://dx.doi.org/10.1109/TDEI.2015.005277]
[12]
D. Xiao, Y. Huang, L. Zhao, C. Qin, H. Shi, and C. Liu, "Domain adaptive motor fault diagnosis using deep transfer learning", IEEE Access, vol. 7, pp. 80937-80949, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2921480]
[13]
J. Zhu, N. Chen, and C. Shen, "A new deep transfer learning method for bearing fault diagnosis under different working conditions", IEEE Sens. J., vol. 20, pp. 8394-8402, 2020.
[http://dx.doi.org/10.1109/JSEN.2019.2936932]
[14]
S. Kim, H. Seo, J. Jung, and H. Yang, "New methods of DGA diagnosis using IEC TC 10 and related databases Part 1: Application of gas-ratio combinations", IEEE Trans. Dielectr. Electr. Insul., vol. 20, pp. 685-690, 2013.
[http://dx.doi.org/10.1109/TDEI.2013.6508773]
[15]
S. Lee, Y. Kim, H. Seo, J. Jung, H. Yang, and M. Duval, "New methods of DGA diagnosis using IEC TC 10 and related databases Part 2: Application of relative content of fault gases", IEEE Trans. Dielectr. Electr. Insul., vol. 20, pp. 691-696, 2013.
[http://dx.doi.org/10.1109/TDEI.2013.6508774]
[16]
M. Duval, "A review of faults detectable by gas-in-oil analysis in trans- formers", IEEE Elec. Insul. Mag., vol. 18, pp. 8-17, 2002.
[http://dx.doi.org/10.1109/MEI.2002.1014963]
[17]
M. Duval, and A. dePabla, "Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases", IEEE Elec. Insul. Mag., vol. 17, pp. 31-41, 2001.
[http://dx.doi.org/10.1109/57.917529]
[18]
I. Atanasova-Höhlein, and C. Schütt, "Gas-in-oil analysis and evaluation criteria for synthetic esters in offshore and traction transformers", IEEE Elec. Insul. Mag., vol. 36, pp. 31-35, 2020.
[http://dx.doi.org/10.1109/MEI.2020.9222632]
[19]
Z. Wu, "An adaptive deep transfer learning method for bearing fault diagnosis", Measurement, vol. 151, pp. 107227-1207235, 2020.
[http://dx.doi.org/10.1016/j.measurement.2019.107227]
[20]
X.A. Di Lin, and J. Zhang, ""Double-bootstrapping source data selection for instance-based transfer learning", Pattern Recognit. Lett., vol. 34, pp. 1279-1285, 2013",
[http://dx.doi.org/10.1016/j.patrec.2013.04.012]

Rights & Permissions Print Cite
Article Metrics
15
1
© 2024 Bentham Science Publishers | Privacy Policy