Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)

A Method of Criteria Selection for Transformer Condition Assessment

Authors
You Song, Zhengqi Wen, Wei Du, Qishen LV
Corresponding Author
You Song
Available Online December 2017.
DOI
https://doi.org/10.2991/mcei-17.2017.53How to use a DOI?
Keywords
Transformer; Condition assessment; FMEA; AHP
Abstract
At the present stage, the selection of criteria for transformer condition assessment is mostly based on experience. The lack of accurate and effective synthetic indicators leads to the potential faults of transformers difficult to obtain timely feedback. In this paper, a method of criteria optimization based on analytic hierarchy process (AHP) was proposed. Firstly, FMEA (failure mode and effects analysis) was used to obtain the degree of occurrence, severity and detection methods of various common faults in transformers. Then, the AHP was used to prioritize the detection methods of common faults based on FMEA. Thus, the optimization model of transformer condition assessment was established. Finally take this method of transformer condition assessment as an example, to prove the feasibility of this method.
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Proceedings
2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
Part of series
Advances in Computer Science Research
Publication Date
December 2017
ISBN
978-94-6252-430-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/mcei-17.2017.53How 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  - You Song
AU  - Zhengqi Wen
AU  - Wei Du
AU  - Qishen LV
PY  - 2017/12
DA  - 2017/12
TI  - A Method of Criteria Selection for Transformer Condition Assessment
BT  - 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SP  - 241
EP  - 246
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.53
DO  - https://doi.org/10.2991/mcei-17.2017.53
ID  - Song2017/12
ER  -