A numerical method to transfer an onshore wind turbine FMEA to offshore operational conditions

Failure Modes Effect Analysis (FMEA), or more specifically, Failure Modes Effect and Criticality Analysis (FMECA) has been accepted as an effective condition monitoring assessment tool used widely by the mili-tary, traditional industries and reliability relevant engineering systems. A successful FMEA assists to identity, evaluate and report component failure modes, their severity and impact on the systems. FMEA has been al-ready applied to onshore wind turbines, but there is a lack of offshore wind turbine applications. FMEA can be quantified by using the metric of Risk Priority Number (RPN), defined as the product of the levels of event severity, occurrence frequency and detectability. This paper presents an approach that allows the application of RPN to offshore wind energy by identifying correction factors to existing onshore RPN values taken from previous research. This approach estimates offshore failure rates for key wind turbine components from onshore data

Sensitivity analysis of observational nudging methodology to reduce error in wind resource assessment (WRA) in the North Sea

Towards the improvement of the mesoscale modeling for offshore wind application, the real time observational nudging capability of the Weather Research and Forecasting (WRF) model has been implemented aiming for enhanced model performance. Utilizing three different horizontal levels of the offshore meteorological mast, FINO3, in the North Sea, wind speed observations were integrated into the model core. The performance of this modified model was then assessed for three different atmospheric stability conditions. Results from this study, illustrate that for all three stratification cases, there is a significant improvement in model performance when using observational nudging showing a reduction in Root Mean Square Error of up to 27% when compared to the observations from FINO1 platform. This study suggests that observational nudging takes a step towards more accurate simulations in wind resource assessment (WRA). © 2018 Elsevier Lt