Aggregate reliability analysis of wind turbine generators. Issue 11 (3rd June 2019)

• Record Type: Journal Article
• Authors: Rezamand, Milad; Carriveau, Rupp; Ting, David S.‐K.; Davison, Matt; Davis, Justin Jeffrey
• Is Part Of: IET renewable power generation. Volume 13:Issue 11(2019)
• Page Start: 1902
• Page End: 1910
• Publication Date: 2019-06-03
• Subjects: remaining life assessment -- investment -- wind power plants -- wind turbines -- power generation reliability -- turbogenerators -- maintenance engineering -- condition monitoring -- failure analysis -- Weibull distribution -- data analysis; aggregate reliability analysis -- wind turbine generators -- North America -- utility‐scale turbines -- originally anticipated lifespan -- accurate estimation -- turbine components -- farm assets -- data records -- nonparametric life data analysis -- Weibull Standard Folio life data analysis -- ALTA Standard Folio life data analysis -- naive prediction interval procedure -- remaining life -- WT generators -- individual generators -- prognostic maintenance -- investment decision support systems -- turbine generator reliability -- useful life -- wind farm owners -- lifetime distribution -- electrical loads; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2018.5909
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : In North America, many utility‐scale turbines are approaching, or are beyond the half‐way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimise the life and value of their farm assets. In this study, data records from a wind farm have been used to estimate the reliability of wind turbine (WT) generators. For this study, non‐parametric life data analysis, Weibull Standard Folio life data analysis, and ALTA Standard Folio life data analysis have been used to predict the reliability of the generators. The naive prediction interval procedure also has been used here to provide an approximate range for the remaining life of each generator. This study provides some insight into how reliable a subset of WT generators is and the lifetime distribution of individual generators. These outcomes may be leveraged further by the research community for companion applications like prognostic maintenance and investment decision support systems. This study also begins to investigate how electrical loads may influence turbine generator reliability. The work also illustrates a valuable example of how to estimate component remaining useful life based on truncated/limited data records.