Title :
Fault prediction of wind turbine by using the SVM method
Author :
Jun-Hyun Shin ; Yun-Seong Lee ; Jin-O Kim
Author_Institution :
Dept. of Electr. Eng., Hanyang Univ., Seoul, South Korea
Abstract :
Wind power is one of the fastest growing renewable energy sources. Wind turbine blades and heights have been increased steadily in the last 10 years in order to increase the capacity of wind power generator. So, the amount of wind turbine energy is increased by increasing the capacity of wind turbine generator, but the preventive, corrective and replacement maintenance cost is increased by that´s reasons. Recently, Condition Monitoring System (CMS) can repair the fault and diagnose of wind turbine that introduce to solve these problems. However, these systems have a problem that cannot predict and diagnose of the wind turbine faults. In this paper, wind turbine fault prediction methodology is proposed by using the SVM method. In the case study, wind turbine fault and external environmental factors are analysed by using the SVM method.
Keywords :
condition monitoring; fault diagnosis; maintenance engineering; power engineering computing; support vector machines; wind turbines; CMS; SVM method; condition monitoring system; corrective maintenance cost; fault prediction; preventive maintenance cost; replacement maintenance cost; wind power generator; wind turbine blades; wind turbine energy; wind turbine fault prediction methodology; Environmental factors; Generators; Kernel; Maintenance engineering; Support vector machines; Temperature; Wind turbines; Fault; Maintenance planning; Support Vector Machine; Wind turbine;
Conference_Titel :
Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on
Conference_Location :
Sapporo
Print_ISBN :
978-1-4799-3196-5
DOI :
10.1109/InfoSEEE.2014.6946258