Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability. (December 2017)

• Record Type: Journal Article
• Authors: Scheu, Matti Niclas; Kolios, Athanasios; Fischer, Tim; Brennan, Feargal
• Is Part Of: Reliability engineering & system safety. Volume 168(2017)
• Page Start: 28
• Page End: 39
• Publication Date: 2017-12
• Subjects: Offshore wind energy -- Operations and maintenance -- Maintenance modelling -- Reliability -- Statistical uncertainty -- Availability; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2017.05.021
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A fully probabilistic O&M modelling approach is proposed. Influence of varying failure distributions at constant failure rate has been investigated. Offshore wind farm availability highly depends on failure distribution. Today applied O&M practise do not cover this sufficiently. Abstract: Offshore wind turbine reliability, one of the industry's biggest sources of uncertainty, is the focus of the present paper. Specifically the impact of uncertain component failure distributions at constant failure rates has been investigated with respect to its implications for wind farm availability. A fully probabilistic offshore wind simulation model has been applied to quantify results; effects shown in this paper underline the significant impact that failure probability distributions have on asset performance evaluation. It was found that wind farm availability numbers may vary in the range up to 20 % just by changing the distributions of failure to a different pattern; in particular those scenarios in which extensive failure accumulation occurred led to significant losses in production. Results are interpreted and discussed mainly from the viewpoint of an offshore wind farm developer, owner and operator, with implications underlined for application in state-of-the-art offshore wind O&M (Operations and Maintenance) models and simulation tools.