Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

Application of Rough Set Theory and Causality Diagram in Transformer Troubleshooting of Distribution Network

Authors
Xuegeng Chen, Huibin Sui
Corresponding Author
Xuegeng Chen
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.129How to use a DOI?
Keywords
Rough set theory, Causal Network, Distribution transformer, Fault- diagnosis.
Abstract
As the hub equipment in the distribution network, the transformer fault is difficult to diagnose because of its complexity and diversity. The reasons causing these failures are very complex and unobvious which make the transformer fault diagnosis and analysis very difficult. If the state of the transformer can be timely determined, pre-detection of hidden dangers, and excluded quickly, the accidents and maintenance costs will be greatly reduced. So the fault diagnosis of the transformer has a very practical value and significance. In this paper, the method which combines the Rough set theory with Causal Network is proposed to find out the faults in the transformer by diagnosing the oil. A streamlined decision table is formed, which can help diagnosing faults accurately and rapidly. Compared with the conventional IEC triple ratio method, the proposed method has higher fault tolerance and diagnosis precision.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/ammee-17.2017.129How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xuegeng Chen
AU  - Huibin Sui
PY  - 2017/06
DA  - 2017/06
TI  - Application of Rough Set Theory and Causality Diagram in Transformer Troubleshooting of Distribution Network
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.129
DO  - https://doi.org/10.2991/ammee-17.2017.129
ID  - Chen2017/06
ER  -