Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data

被引:70
作者
Dai, Juchuan [1 ,2 ]
Yang, Wenxian [2 ,3 ]
Cao, Junwei [1 ]
Liu, Deshun [1 ]
Long, Xing [4 ]
机构
[1] Hunan Univ Sci & Technol, Sch Electromech Engn, Xiangtan 411201, Peoples R China
[2] Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[3] Hunan Inst Engn, Hunan Prov Cooperat Innovat Ctr Wind Power Equipm, Xiangtan 411104, Peoples R China
[4] XEMC Windpower Co Ltd, Xiangtan 411000, Peoples R China
关键词
Wind turbines; Ageing assessment; Performance degradation; SCADA data; KERNEL DENSITY-ESTIMATION; FAULT-DIAGNOSIS; PREDICTION; BEM; OPTIMIZATION; MAINTENANCE; PERFORMANCE; MODEL;
D O I
10.1016/j.renene.2017.03.097
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Ageing of a wind turbine and its components is inevitable. It will affect the reliability and power generation of the turbine over time. Therefore, performing the ageing assessment of wind turbines is of significance not only to optimize the operation and maintenance strategy of the wind turbine but also to improve the management of a wind farm. However, in contrast to the significant number of research on wind turbine condition monitoring and reliability analysis, little effort was made before to investigate the ageing led performance degradation issue of wind turbines over time. To fill such a technology gap, four SCADA-based wind turbine ageing assessment criteria are proposed in this paper for measuring the ageing resultant performance degradation of the turbine. With the aid of these four criteria, a reliable information fusion based wind turbine ageing assessment method is developed and verified in the end using the real wind farm SCADA data. It has been shown that the proposed method is effective and reliable in performing the ageing assessment of a wind turbine. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:199 / 208
页数:10
相关论文
共 34 条
[1]   A brief status on condition monitoring and fault diagnosis in wind energy conversion systems [J].
Amirat, Y. ;
Benbouzid, M. E. H. ;
Al-Ahmar, E. ;
Bensaker, B. ;
Turri, S. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2009, 13 (09) :2629-2636
[2]  
[Anonymous], 2011, ENERGY SUSTAIN
[3]   Failure Modes and Effects Analysis (FMEA) for wind turbines [J].
Arabian-Hoseynabadi, H. ;
Oraee, H. ;
Tavner, P. J. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2010, 32 (07) :817-824
[4]   An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades [J].
Besnard, Francois ;
Bertling, Lina .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2010, 1 (02) :77-83
[5]   Time Adaptive Conditional Kernel Density Estimation for Wind Power Forecasting [J].
Bessa, Ricardo J. ;
Miranda, Vladimiro ;
Botterud, Audun ;
Wang, Jianhui ;
Constantinescu, Emil M. .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2012, 3 (04) :660-669
[6]   Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals [J].
Chen, Jinglong ;
Pan, Jun ;
Li, Zipeng ;
Zi, Yanyang ;
Chen, Xuefeng .
RENEWABLE ENERGY, 2016, 89 :80-92
[7]   Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model [J].
Dai, J. C. ;
Hu, Y. P. ;
Liu, D. S. ;
Long, X. .
RENEWABLE ENERGY, 2011, 36 (03) :1095-1104
[8]   Modelling and analysis of direct-driven permanent magnet synchronous generator wind turbine based on wind-rotor neural network model [J].
Dai, J-C ;
Hu, Y-P ;
Liu, D-S ;
Wei, J. .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY, 2012, 226 (A1) :62-72
[9]   Research on power coefficient of wind turbines based on SCADA data [J].
Dai, Juchuan ;
Liu, Deshun ;
Wen, Li ;
Long, Xin .
RENEWABLE ENERGY, 2016, 86 :206-215
[10]  
Feng Y., 2011, European Wind Energy Conference and Exhibition 2011, P17