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

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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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  -