Fault prediction of wind turbine by using the SVM method

被引:0
作者
Shin, Jun-Hyun [1 ]
Lee, Yun-Seong [1 ]
Kim, Jin-O [1 ]
机构
[1] Hanyang Univ, Dept Elect Engn, Seoul 133791, South Korea
来源
2014 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, ELECTRONICS AND ELECTRICAL ENGINEERING (ISEEE), VOLS 1-3 | 2014年
关键词
Maintenance planning; Fault; Support Vector Machine; Wind turbine;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1923 / 1926
页数:4
相关论文
共 6 条
[1]  
[Anonymous], 1973, Pattern Classification and Scene Analysis
[2]   Fault classification and section identification of an advanced series-compensated transmission line using support vector machine [J].
Dash, P. K. ;
Samantaray, S. R. ;
Panda, Ganapati .
IEEE TRANSACTIONS ON POWER DELIVERY, 2007, 22 (01) :67-73
[3]   An Adaptive Approach Based on KPCA and SVM for Real-Time Fault Diagnosis of HVCBs [J].
Ni, Jianjun ;
Zhang, Chuanbiao ;
Yang, Simon X. .
IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (03) :1960-1971
[4]  
Salat Robert, 2004, IEEE T POWER SYSTEMS, V19
[5]  
Shin Jun-Hyun, 2013, KIEE FALL C 2013
[6]  
Yaqub M. F., 2013, IEEE T RELIABILITY, V62