Fleet-level opportunistic maintenance for large-scale wind farms integrating real-time prognostic updating

被引:26
作者
Xia, Tangbin [1 ]
Dong, Yifan [1 ]
Pan, Ershun [1 ]
Zheng, Meimei [1 ]
Wang, Hao [1 ]
Xi, Lifeng [1 ]
机构
[1] Shanghai Jiao Tong Univ, SJTU Fraunhofer Ctr, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Opportunistic maintenance; Prognosis updating; Fleet structure; Power production loss; Wind farm; TURBINES; SYSTEMS; COST;
D O I
10.1016/j.renene.2020.08.072
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Operation and maintenance (O&M) of wind farms has become progressively more important for renewable energy. The key challenge is that each modern large-scale wind farm normally consists of many wind turbines in parallel, while each complex turbine also contains diverse series of components. Traditional maintenance policies cannot handle such a complex system, let alone that each individual component undergoes different degradations. To reduce the scheduling complexity and maintenance cost, a fleet maintenance cost saving (FMCS) policy is developed to optimize condition-based opportunistic maintenance. Real-time condition data for each component is utilized to update its failure prognostic for avoiding the individual variation in the degradation process. On this basis, the whole wind farm is constructed as a fleet structure with series-parallel components. Power production loss within the same turbine and repeated personnel dispatch among other parallel turbines are analyzed to reduce the total maintenance cost efficiently. Through the case study, the framework with real-time prognostic updating and FMCS scheduling policy in component/fleet levels has been proven its economic advantages for future large-scale wind farms. (C) 2020 Published by Elsevier Ltd.
引用
收藏
页码:1444 / 1454
页数:11
相关论文
共 33 条
[1]   An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings [J].
Bangalore, Pramod ;
Tjernberg, Lina Bertling .
IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (02) :980-987
[2]   Stochastic Optimization of Maintenance and Operations Schedules Under Unexpected Failures [J].
Basciftci, Beste ;
Ahmed, Shabbir ;
Gebraeel, Nagi Z. ;
Yildirim, Murat .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (06) :6755-6765
[3]   Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines [J].
Carroll, James ;
McDonald, Alasdair ;
McMillan, David .
WIND ENERGY, 2016, 19 (06) :1107-1119
[4]   Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes [J].
Cheng, Fangzhou ;
Qu, Liyan ;
Qiao, Wei ;
Hao, Liwei .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 66 (06) :4738-4748
[5]   Advanced logistics planning for offshore wind farm operation and maintenance activities [J].
Dalgic, Yalcin ;
Lazakis, Iraklis ;
Dinwoodie, Iain ;
McMillan, David ;
Revie, Matthew .
OCEAN ENGINEERING, 2015, 101 :211-226
[6]   Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds [J].
Ding, Fangfang ;
Tian, Zhigang .
RENEWABLE ENERGY, 2012, 45 :175-182
[7]   An updated review: white etching cracks (WECs) and axial cracks in wind turbine gearbox bearings [J].
Evans, M. -H. .
MATERIALS SCIENCE AND TECHNOLOGY, 2016, 32 (11) :1133-1169
[8]   Multistream sensor fusion-based prognostics model for systems with single failure modes [J].
Fang, Xiaolei ;
Paynabar, Kamran ;
Gebraeel, Nagi .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2017, 159 :322-331
[9]   Multidimensional Tensor-Based Inductive Thermography With Multiple Physical Fields for Offshore Wind Turbine Gear Inspection [J].
Gao, Bin ;
He, Yunze ;
Woo, Wai Lok ;
Tian, Gui Yun ;
Liu, Jia ;
Hu, Yihua .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2016, 63 (10) :6305-6315
[10]   Prognostic Degradation Models for Computing and Updating Residual Life Distributions in a Time-Varying Environment [J].
Gebraeel, Nagi ;
Pan, Jing .
IEEE TRANSACTIONS ON RELIABILITY, 2008, 57 (04) :539-550