Research on Fault Warning of doubly Fed Wind Power Generator based on LS-SVM

DOI

10.2991/eeeis-17.2017.23How to use a DOI?

Keywords

Keyword: Double-fed wind generator, electric generator, Least squares support vector machines, fault early warning.

Abstract

According to the problem that difficult to build accurate model of equipment due to DFIG complex operation condition and strong coupling characteristics of multi state variables , a intelligent data mining method was applied to the early fault warning and diagnosis of wind turbine equipment. The wind turbine typical operating characteristics have been analyzed and a method based on least squares support vector machine (LS-SVM) of the double fed wind turbine fault warning has been presented. Combine the history data of a wind turbine generator of unit 18 of a wind farm in Hami Xinjiang wind power line collection area, the proposed method is verified and analyzed by Matlab. The study results prove that the prediction method has high estimation accuracy, can promptly identify fault of double fed wind generator in operation, also the method is applicable to fault diagnosis of equipment of doubly fed wind generator, thus, it has certain engineering application value.

Copyright

© 2017, 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  - Sheng-Yu NIU
AU  - Bo-Wen LIU
AU  - Xin-Yan ZHANG
PY  - 2017/09
DA  - 2017/09
TI  - Research on Fault Warning of doubly Fed Wind Power Generator based on LS-SVM
BT  - Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017)
PB  - Atlantis Press
SP  - 158
EP  - 163
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-17.2017.23
DO  - 10.2991/eeeis-17.2017.23
ID  - NIU2017/09
ER  -