An Approach of Quantifying Gear Fatigue Life for Wind Turbine Gearboxes Using Supervisory Control and Data Acquisition Data

被引:20
|
作者
Qiu, Yingning [1 ]
Chen, Lang [1 ]
Feng, Yanhui [1 ]
Xu, Yili [2 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Energy & Power Engn, Nanjing 210094, Jiangsu, Peoples R China
[2] Zhejiang Windey Ltd Share Ltd, 22F,Bldg A,West Lake Int Plaza S&T 391,Wener Rd, Hangzhou 310012, Zhejiang, Peoples R China
来源
ENERGIES | 2017年 / 10卷 / 08期
基金
中国国家自然科学基金;
关键词
wind turbine (WT); fatigue life; gearbox; supervisory control and data acquisition (SCADA) data; DYNAMIC-ANALYSIS; DRIVE TRAIN; SYSTEM; DAMAGE; LOADS;
D O I
10.3390/en10081084
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Quantifying wind turbine (WT) gearbox fatigue life is a critical problem for preventive maintenance when unsolved. This paper proposes a practical approach that uses ten minutes' average wind speed of Supervisory Control and Data Acquisition (SCADA) data to quantify a WT gearbox's gear fatigue life. Wind turbulence impacts on gearbox fatigue are studied thoroughly. Short-term fatigue assessment for the gearbox is then performed using linear fatigue theory by considering WT responses under external and internal excitation. The results shows that for a three stage gearbox, the sun gear in the first stage and pinions in the 2nd and 3rd stage are the most vulnerable parts. High mean wind speed, especially above the rated range, leads to a high risk of gearbox fatigue damage. Increase of wind turbulence may not increase fatigue damage as long as a WT has an instant response to external excitation. An approach of using SCADA data recorded every ten minutes to quantify gearbox long-term damages is presented. The calculation results show that the approach effectively presents gears' performance degradation by quantifying their fatigue damage. This is critical to improve WT reliability and meaningful for WT gearbox fatigue assessment theory. The result provides useful tools for future wind farm prognostic maintenance.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data
    Mylonas, Charilaos
    Abdallah, Imad
    Chatzi, Eleni
    [J]. WIND ENERGY, 2021, 24 (10) : 1122 - 1139
  • [2] Probabilistic analysis of gear flank micro-pitting risk in wind turbine gearbox using supervisory control and data acquisition data
    Al-Tubi, Issa
    Long, Hui
    Tavner, Peter
    Shaw, Brian
    Zhang, Jishan
    [J]. IET RENEWABLE POWER GENERATION, 2015, 9 (06) : 610 - 617
  • [3] A Method for Abnormal Data Recognition of Wind Turbine Supervisory Control and Data Acquisition Systems
    Li, Te
    Wang, Rongxi
    Gao, Jianmin
    [J]. Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2024, 58 (03): : 106 - 116
  • [4] Health condition assessment of wind turbine generators based on supervisory control and data acquisition data
    Li, Junqing
    Li, Qiujia
    Zhu, Jianguo
    [J]. IET RENEWABLE POWER GENERATION, 2019, 13 (08) : 1343 - 1350
  • [5] Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning
    Liu, Yao
    Cheng, Han
    Kong, Xianguang
    Wang, Qibin
    Cui, Huan
    [J]. ENERGY SCIENCE & ENGINEERING, 2019, 7 (06) : 2633 - 2645
  • [6] Classification of Highly Imbalanced Supervisory Control and Data Acquisition Data for Fault Detection of Wind Turbine Generators
    Maldonado-Correa, Jorge
    Valdiviezo-Condolo, Marcelo
    Artigao, Estefania
    Martin-Martinez, Sergio
    Gomez-Lazaro, Emilio
    [J]. ENERGIES, 2024, 17 (07)
  • [7] Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition
    Chen, Bindi
    Matthews, Peter C.
    Tavner, Peter J.
    [J]. IET RENEWABLE POWER GENERATION, 2015, 9 (05) : 503 - 513
  • [8] SUPERVISORY CONTROL AND DATA ACQUISITION
    GAUSHELL, DJ
    DARLINGTON, HT
    [J]. PROCEEDINGS OF THE IEEE, 1987, 75 (12) : 1645 - 1658
  • [9] Pitch fault diagnosis of wind turbines in multiple operational states using supervisory control and data acquisition data
    Wei, Lu
    Qian, Zheng
    Yang, Cong
    Pei, Yan
    [J]. WIND ENGINEERING, 2019, 43 (05) : 443 - 458
  • [10] Alarms management by supervisory control and data acquisition system for wind turbines
    Ramirez I.S.
    Mohammadi-Ivatloo B.
    Márquez F.P.G.
    [J]. Eksploatacja i Niezawodnosc, 2021, 23 (01) : 110 - 116