Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

The 750kV Transformer Condition Assessment Based on Improved Grey-Target

Authors
Xi He, Ruiping Zhang, Haiying Dong
Corresponding Author
Xi He
Available Online November 2016.
DOI
10.2991/aiie-16.2016.116How to use a DOI?
Keywords
grey target theory; condition assessment; 750kV transformer
Abstract

In view of the 750kV transformer that has high voltage, high insulation level, and heavy load all the years.This paper analysis the monitor data to obtain characteristic parameters which can reflect the transformer healthy conditions. On the basis of traditional Grey target theory, It uses the improved Grey target theory to give the different weight of each index, and calculates the approaching degree to make health assessment, so as to obtain the purpose of determine transformer-health.

Copyright
© 2016, 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/).

Download article (PDF)

Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.116How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Xi He
AU  - Ruiping Zhang
AU  - Haiying Dong
PY  - 2016/11
DA  - 2016/11
TI  - The 750kV Transformer Condition Assessment Based on Improved Grey-Target
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 499
EP  - 502
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.116
DO  - 10.2991/aiie-16.2016.116
ID  - He2016/11
ER  -