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Application of gamma process and maintenance cost for fatigue damage of wind turbine blade

Application of gamma process and maintenance cost for fatigue damage of wind turbine blade

Zhang, Chizhi and Tee, Kong Fah ORCID: 0000-0003-3202-873X (2019) Application of gamma process and maintenance cost for fatigue damage of wind turbine blade. In: Energy Procedia. Elsevier, pp. 3729-3734. ISSN 1876-6102 (doi:https://doi.org/10.1016/j.egypro.2019.01.884)

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Abstract

The blades of offshore wind turbines accumulate structural damage such as fatigue due to aerodynamic loading by various wind speeds during their service time, leading to premature structural failures. This paper investigates the fatigue damage by the blade element momentum theory (BEMT) method and the cost of operation and maintenance by the integrating models. Three prediction models have been proposed for lifetime performance assessment and management of wind turbine blade, i.e. fatigue prediction model (FPM), the reliable stochastic model (SRM) and cost benefit model (CBM). The fatigue model is discussed to reproduce the fatigue damage evolution in composite blades subjected to aerodynamic loadings by cyclical winds. The lifetime probability of fatigue failure of the composite blades is estimated by stochastic deterioration modelling such as gamma process. On the basis of the cost model, an optimised maintenance policy is determined to make optimal maintenance decision for the composite blades. A numerical example is employed to investigate the effectiveness of predicting fatigue damage and estimating the probability of fatigue failure to determine an opportunistic maintenance policy. The results from the numerical study show that the stochastic gamma process together with the fatigue models can provide a useful tool for remaining useful life predictions and optimum maintenance strategies of the composite blades of offshore wind turbines.

Item Type: Conference Proceedings
Title of Proceedings: Energy Procedia
Uncontrolled Keywords: wind turbine blade, fatigue, stochastic modelling, cost model, reliability
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 19 Sep 2020 00:20
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/26554

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