Fault Diagnosis and Forecast of Substation Equipment Temperature Based on Fuzzy C Means Clustering Algorithm

DOI

10.2991/icmse-15.2015.222How to use a DOI?

Keywords

Power equipment; temperature; fuzzy C means.

Abstract

The normal operation of power equipment is directly related to the normal life of people in the relevant areas and the normal operation of the enterprise. Regardless of the quality factor of equipment, the most important factor affecting the normal operation of the equipment is the working temperature of the equipment.The traditional temperature measurement of electric power equipment is relying on manual inspection. This method not only has a large workload, high cost, but also low efficiency. The most important is that it cannot predict the temperature of the equipment in a timely manner. After considering the above factors, the fuzzy C means clustering algorithm is used to diagnose and forecast the substation equipment.

Copyright

© 2015, 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  - Jun Li
AU  - Jiangwen Xiao
AU  - Jing Wu
AU  - Chun Chen
AU  - Yiwen Xiao
PY  - 2015/12
DA  - 2015/12
TI  - Fault Diagnosis and Forecast of Substation Equipment Temperature Based on Fuzzy C Means Clustering Algorithm
BT  - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
PB  - Atlantis Press
SP  - 1215
EP  - 1219
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-15.2015.222
DO  - 10.2991/icmse-15.2015.222
ID  - Li2015/12
ER  -