Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics

Establishment and Implementation of Heating State Assessment of High-voltage Disconnecting Switch Based on RBF Neural Network

Authors
Haokai Xie
Corresponding Author
Haokai Xie
Available Online August 2016.
DOI
10.2991/emcpe-16.2016.28How to use a DOI?
Keywords
Disconnecting Switching, Overheat Fault, RBF Neural Network, State Assessment
Abstract

Currently, it is difficult to detect the overheat faults of high-voltage disconnecting switch and send out early warning signals effectively and immediately. Hence, an early-warning model was built by using radical basis function (RBF) neural network. The model takes three factors into consideration, including the ratio of load current and rated current, pollution grade and ambient temperature. The test result showed that the overall accuracy rate reached 94.44%, and it can detect the overheat defects with 100%.

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/).

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Volume Title
Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics
Series
Advances in Engineering Research
Publication Date
August 2016
ISBN
978-94-6252-197-1
ISSN
2352-5401
DOI
10.2991/emcpe-16.2016.28How 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  - Haokai Xie
PY  - 2016/08
DA  - 2016/08
TI  - Establishment and Implementation of Heating State Assessment of High-voltage Disconnecting Switch Based on RBF Neural Network
BT  - Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics
PB  - Atlantis Press
SP  - 131
EP  - 136
SN  - 2352-5401
UR  - https://doi.org/10.2991/emcpe-16.2016.28
DO  - 10.2991/emcpe-16.2016.28
ID  - Xie2016/08
ER  -