| Title |
Fault prediction of wind turbine and Generation benefit evaluation by using the SVM method |
| Authors |
Jun-Hyun Shin ; Yun-Seong Lee ; Sung-Yul Kim ; Jin-O Kim |
| DOI |
http://dx.doi.org/10.5207/JIEIE.2014.28.5.060 |
| Keywords |
Support Vector Machine ; Maintenance ; Wind Turbine ; Prediction |
| Abstract |
Wind power is one of the fastest growing renewable energy sources. The blades length and tower height of wind turbine have been growing steadily in the last 10 years in order to increase the output amount of wind power energy. The amount of wind turbine energy is increased by increasing the capacity of wind turbine, but the costs of preventive, corrective and replacement maintenance are also increased accordingly. Recently, Condition Monitoring System that can repair the fault diagnose and repair of wind turbine in the real-time. However, these system have a problem that cannot predict and diagnose of the fault. In this paper, wind turbine predict methodology is proposed by using the SVM method. In the case study, correlation analysis between wind turbine fault and external environmental factors is performed by using the SVM method. |