Open AccessAggregate reliability analysis of wind turbine generators
Author(s) -
Rezamand Milad,
Carriveau Rupp,
Ting David S.K.,
Davison Matt,
Davis Justin Jeffrey
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.5909
Subject(s) - turbine , reliability engineering , reliability (semiconductor) , weibull distribution , wind power , parametric statistics , computer science , life extension , interval (graph theory) , engineering , power (physics) , statistics , electrical engineering , mathematics , quantum mechanics , combinatorics , mechanical engineering , gerontology , medicine , physics
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.