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A fast fault diagnosis method for wind turbine generator system based on rough set-decision tree

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3 Author(s)
Huizhong Wang ; School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, China ; Anqun Peng ; Xiaolan Wang

With rough set theory for knowledge reduction capability and C4.5 decision tree algorithm for fast classification of strengths, an improved rough set-decision tree model for fault diagnosis of wind generation system is built. The results show that the proposed method can not only decreases the workload of feature datum extraction, but also identifies the fault patterns rapidly and accurately, and it exhibits better engineering practicality comparing with the C4.5-based method.

Published in:

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Date of Conference:

8-10 Aug. 2011