Data-Informed Lifetime Reliability Prediction for Offshore Wind Farms

Offshore wind operation and maintenance (O&M) costs can reach up to 1/3 of the overall project costs. In order to accelerate the deployment of these clean energy assets, costs need to come down. This requires, a good understanding of the different operations along with a robust planning, maintenance and monitoring strategy. Asset management tools have been developed, which require reliability inputs, able to estimate the lifetime operational expenditure (OPEX) and optimize the maintenance strategies for the assets. The lack of large datasets with offshore wind failure rate data in the literature increases the uncertainty in the estimations made by those tools. This paper aims to compare whether the publicly available data could provide an accurate information of the lifetime reliability predictions of the assets. It initially uses a generic average failure rate, taken from literature to model the wind farm; as most wind farm developers will not have any detailed understanding of the reliability of the asset prior to construction. It then uses a more detailed, turbine-specilic model, taking into account reliability data from an operational wind farm. Results show a small overall difference when the model uses the data-informed parameters, by up to 0.4% in the overall availability. Moreover, it is shown that the use of generic values can create more pessimistic results compared to the data-informed data. The results of the paper are of interest to offshore wind farm developers and operators aiming to improve their lifetime reliability estimations and reduce the O&M costs of the offshore wind farms.