@inproceedings{a9234b690d294118af533aff2a14868f,
title = "Mechanical life prognosis of high voltage circuit breakers based on support vector machine",
abstract = "Mechanical fault is one of the main faults occurring during the life cycle of high-voltage circuit breakers (HVCBs), which has a significant influence on the reliability of the electrical power system. In this paper, the mechanical prediction algorithm for HVCBs based on support vector machine (SVM) was studied. Firstly, we used a sliding time window (STW) method to extract features of the travel curves of the movable contacts and coil current curves of HVCBs. Then the historic data were used to learn a support vector regression machine and finally to predict the new curves. In the end, the mechanical life experiment data of a HVCB were applied to validate the feasibility of the algorithm. The results showed that the proposed algorithm could predict the mechanical condition of HVCBs successfully.",
keywords = "high voltage circuit breaker, mechanical life, support vector machine, time series",
author = "Xin Zhang and Ronghui Huang and Senjing Yao and Gaoyang Li and Linlin Zhong and Xiaohua Wang",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th International Conference on Natural Computation, ICNC 2015 ; Conference date: 15-08-2015 Through 17-08-2015",
year = "2016",
month = jan,
day = "8",
doi = "10.1109/ICNC.2015.7378084",
language = "英语",
series = "Proceedings - International Conference on Natural Computation",
publisher = "IEEE Computer Society",
pages = "749--753",
editor = "Zheng Xiao and Zhao Tong and Kenli Li and Xingwei Wang and Keqin Li",
booktitle = "2015 11th International Conference on Natural Computation, ICNC 2015",
}