[1]TONY B.Wind Energy Handbook[M].Beijing: Science Press,2009.
[2]杨锡运,郭鹏,岳俊红.风力发电机组故障诊断技术[M].北京:中国水利水电出版社,2015.
YANG Yunxi,GUO Peng,YUE Junhong.Wind Turbine Fault Diagnosis Technology[M]. Beijing:China Water & Power Press,2015.
[3]李辉,胡姚刚,李洋,等.大功率并网风电机组状态监测与故障诊断研究综述[J].电力自动化设备,2016,36(1):6-16.
LI Hui,HU Yaogang,LI Yang,et al.Overview of Condition Monitoring and Fault Diagnosis for Grid-connected High-power Wind Turbine Unit[J]. Electric Power Automation Equipment,2016,36(1):6-16.
[4]LINDEN D V D,SITTER G D,VERBELEN T,et al. Towards an Evolvable Data Management System for Wind Turbines[J].Computer Standards & Interfaces,2017,51:87-94.
[5]赵洪山,张路朋.基于可靠度的风电机组预防性机会维修策略[J].中国电机工程学报,2014,34(22):3777-3783.
ZHAO Hongshan,ZHANG Lupeng. Preventive Opportunistic Maintenance Strategy for Wind Turbines Based on Reliability[J].Proceedings of the CSEE,2014,34(22):3777-3783.
[6]WYMORE M L,DAM J E V,CEYLAN H,et al.A Survey of Health Monitoring Systems for Wind Turbines[J]. Renewable & Sustainable Energy Reviews,2015,52:976-990.
[7]SCHLECHTINGEN M,SANTOS I F,ACHICHE S. Wind Turbine Condition Monitoring Based on SCADA Data Using Normal Behavior Models.Part 1:System Description[J]. Applied Soft Computing,2013,13(1):259-270.
[8]SCHLECHTINGEN M,SANTOS I . Wind Turbine Condition Monitoring Based on SCADA Data Using Normal Behavior Models. Part 2:Application Examples[J]. Applied Soft Computing,2014,14(1):447-460.
[9]李状,柳亦兵,马志勇,等.结合C-均值聚类的自适应共振神经网络在风电机组齿轮箱故障诊断中的应用[J].动力工程学报,2015,35(8):646-651.
LI Zhuang,LIU Yibing,MA Zhiyong,et al.Application of ART2 Neural Network Combined with C-means Clustering in Fault Diagnosis of Wind Turbine Gearbox[J]. Journal of Chinese Society of Power Engineering,2015,35(8):646-651.
[10]向玲,鄢小安.基于小波包的EITD风力发电机组齿轮箱故障诊断[J].动力工程学报,2015,35(3):205-212.
XIANG Ling,YAN Xiaoan.Fault Diagnosis of Wind Turbine Gearbox Based on EITD-WPT Method[J]. Journal of Chinese Society of Power Engineering,2015,35(3):205-212.
[11]黎涛,唐明珠,谭欣星.基于CLSSVM的风电机组齿轮箱故障诊断[J].可再生能源,2015,33(2):232-237.
LI Tao,TANG Mingzhu,TAN Xinxing.Fault Diagnosis of Wind Turbine Gearbox Based on Cost-sensitive Least Squares Support Vector Machine[J].Renewable Energy Resources,2015,33(2):232-237.
[12]顾煜炯,贾子文,王瑞,等.基于改进的多元离群检测方法的风机齿轮箱早期故障诊断[J].中国机械工程,2016,27(14):1905-1910.
GU Yujiong,JIA Ziwen,WANG Rui,et al.Early Fault Diagnosis for Wind Turbine Gearbox Based on Improved Multivariate Outlier Detection[J].China Mechanical Engineering,2016,27(14):1905-1910.
[13]孟宗,李晶,龙海峰,等.基于压缩信息特征提取的滚动轴承故障诊断方法[J].中国机械工程,2017,28(7):806-812.
MENG Zong,LI Jing,LONG Haifeng,et al.Fault Diagnosis Method for Rolling Bearings Based on Compression Information Feature Extractions[J].China Mechanical Engineering,2017,28(7):806-812.
[14]郭厚明,行志刚,荆双喜.无量纲参数在矿用低速重载齿轮故障诊断中的应用[J].煤炭科学技术,2006,34(8):28-31.
GUO Houming,XING Zhigang,JIN Shuangxi. Dimensionless Parameters Applied to Fault Diagnosis of Mine Low Speed Heavy Loaded Gear[J].Coal Science and Technology,2006,34(8):28-31.
[15]尹诗,余忠源,孟凯峰,等.基于非线性状态估计的风电机组变桨控制系统故障识别[J].中国电机工程学报,2014,34(S1):160-165.
YIN Shi,YU Zhongyuan,MENG Kaifeng,et al.Fault Identification of Pitch Control System of Wind Turbine Based on Nonlinear State Estimation [J]. Proceedings of the CSEE,2014,34(S1):160-165.
[16]郭鹏, INFIELD D, 杨锡运.风电机组齿轮箱温度趋势状态监测及分析方法[J].中国电机工程学报, 2011, 31(32):129-136.
GUO Peng, INFIELD D, YANG Xiyun.Wind Turbine Gearbox Condition Monitoring Using Temperature Trend Analysis[J].Proceedings of the CSEE,2011,31(32):129-136.
[17]何群,李磊,江国乾,等.基于PCA和多变量极限学习机的轴承剩余寿命预测[J].中国机械工程,2014,25(7):984-989.
HE Qun, LI Lei,JIANG Guoqian, et al.Residual Life Predictions for Bearings Based on PCA and MELM[J]. China Mechanical Engineering,2014,25(7):984-989.
[18]CHANDOLA V,BANERJEE A,KUMAR V. Anomaly Detection:a Survey[J]. ACM Computing Surveys,2009,41(3):1-58.
[19]王淑芬.应用统计学[M].北京:北京大学出版社,2011.
WANG Shufen.Applied Statistics[M].Beijing:Peking University Press,2011.
[20]刘涛,刘吉臻,吕游,等.基于多元状态估计和偏离度的电厂风机故障预警[J].动力工程学报,2016,36(6):454-460.
LIU Tao,LIU Jizhen,LYU You,et al.Early Fault Warning of Power Plant Fans Based on MSET and the Deviation Degree[J].Journal of Chinese Society of Power Engineering,2016,36(6):454-460.