Improved DGA Methods of Power Transformer Fault Diagnosis: A Review

DOI

10.2991/iceep-18.2018.321How to use a DOI?

Keywords

DGA technique, Power Transformer Fault Diagnose, AI method, statistics method, new diagnostic methods. Shanghai University of Electric Power

Abstract

Power transformers are key components in the stable operation of electric power system. Reasonable arrangements of maintenance strategy according to transformers’ working condition can decrease the loss lead by transformers’ faults. The dissolved gas analysis (DGA) is the widest technique used in transformers’ fault diagnosis and condition monitoring. Based on the conception of DGA technique, researchers and engineers have developed many methods and standard for faults diagnosis, such as IEC standard code, IEEE ratio, and Duval triangle method. They are practical and easy to use, but still face some problems. Many improvements for DGA have been carried out for improving the diagnostic accuracy. Artificial Intelligence (AI) method, statistics method or new diagnostic ways are the hot field for research. This paper has introduced the improved DGA methods of power transformer fault diagnosis in recent years and put forward some technical outlook of this field

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chen-hang Ge
AU  - Hao-yang Cui
AU  - Si-jia Huo
AU  - Wen-cheng Guo
AU  - Hong-wei Ma
AU  - Lun-ming Qin
PY  - 2018/09
DA  - 2018/09
TI  - Improved DGA Methods of Power Transformer Fault Diagnosis: A Review
BT  - Proceedings of the 2018 7th International Conference on Energy and Environmental Protection (ICEEP 2018)
PB  - Atlantis Press
SP  - 1765
EP  - 1768
SN  - 2352-5401
UR  - https://doi.org/10.2991/iceep-18.2018.321
DO  - 10.2991/iceep-18.2018.321
ID  - Ge2018/09
ER  -