Posters

Abstract

Premature failure of the main wind turbine sub-systems such as generators, gearboxes or transformers can result in prolonged downtime, production losses and expensive repairs. The implications of such failures for fleet owners and operators can become extremely significant, as the overall financial liability is potentially high. Decisions concerning the choice of service contract, preferred service provider, level of insurance, required spare parts and tooling, or whether to extend the operational life of the turbine are all strongly influenced by expectations of the future reliability of the turbines. Therefore, the capability to project failure rates over relatively long time periods ranging from one to ten years becomes extremely important. Classical reliability theory is readily available for addressing such problems, including well proven techniques such as MTTF (Mean-Time-To-Failure) assessment and Weibull modelling. However, the practical application of such techniques is often limited due to the high effort required to prepare well classified historical failure datasets, or even a complete absence of such failure data. In this presentation we will demonstrate a method that has been developed to deals with such limitations in a pragmatic manner, allowing component failure risk to be calculated with quantified level of uncertainty. The method has been successfully implemented for an aging fleet of around 100 turbines and the results are used to provide decision support for centralised maintenance management.