Wind Farm Operation And Maintenance Optimization Using Big Data

被引:16
|
作者
Helsen, Jan [1 ]
Peeters, Cedric [2 ]
Doro, Peter [2 ]
Ververs, Eveline [3 ]
Jordaens, Pieter Jan [4 ]
机构
[1] Vrije Univ Brussel, OWI Lab, Dept Mech Engn, Pl Laan 2, B-1050 Brussels, Belgium
[2] Vrije Univ Brussel, Dept Mech Engn, Pl Laan 2, B-1050 Brussels, Belgium
[3] Univ Antwerp, Middelheimlaan 1, B-2020 Antwerp, Belgium
[4] OWl Lab, Celestijnenlaan 300, B-3001 Heverlee, Belgium
关键词
wind turbine; wind energy; prognostics; failure; vibrations; offshore wind;
D O I
10.1109/BigDataService.2017.27
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the current electricity production mix wind energy is claiming a significant part. In order to guarantee stable electricity production predictability of the wind farm operational behaviour is essential. Big data approaches have the potential for a significant role in realizing this goal. In order to gain insights in turbine operational behaviour it is necessary to obtain a farm wide dataset, containing the operational sensor data of the different machines and context information such as maintenance data. Advanced analytics can use this data for understanding normal and deviating turbine operational behaviour. These insights will help in optimizing the operation and maintenance strategy of the farm. This paper gives an overview of our big data approach for data-storage and illustrates some of our data-analytics research tracks for gaining insights in the underlying failure mechanisms of turbines.
引用
收藏
页码:179 / 184
页数:6
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