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Recommended practices for wind farm data collection and reliability assessment for O&M optimization

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Journal , Contribution for newspaper or weekly magazine 01 Oct 2017 Denmark, Norway Publisher:Elsevier BVJournal:Energy Procedia, volume 137, pages 358-365 (issn: 1876-6102,

Authors: Hahn, Berthold; Welte, Thomas Michael; Faulstich, Stefan; Bangalore, Pramod; Boussion, Cyril; Harrison, K; Miguelanez-Martin, Emilio; +7 Authors

doi: 10.1016/j.egypro.2017.10.360 , 10.24406/publica-r-251998

handle: 11250/2655262

Recommended practices for wind farm data collection and reliability assessment for O&M optimization

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Abstract

The paper provides a brief overview of the aims and main results of IEA Wind Task 33. IEA Wind Task 33 was an expert working group with a focus on data collection and reliability assessment for O & M optimization of wind turbines. The working group started in 2012 and finalized the work in 2016. The complete results of IEA Wind Task 33 are described in the expert group report on recommended practices for "Wind farm data collection and reliability assessment for O & M optimization" which will be published by IEA Wind in 2017. This paper briefly presents the background of the work, the recommended process to identify necessary data, and appropriate taxonomies structuring and harmonizing the collected entries. Finally, the paper summarizes the key findings and recommendations from the IEA Wind Task 33 work.

Countries

Denmark, Norway

Related Organizations

Delft University of Technology
Netherlands
Norwegian University of Science and Technology
Norway
Energy Research Centre of the Netherlands
Netherlands
Atkins (United States)
United States
Chalmers University of Technology
Sweden

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Keywords

maintenance optimization, reliability data, IEA Wind, wind turbine, Maintenance optimization, Reliability analyses, Reliability data, reliability analyses, Wind turbine

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	14
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%