Abstract
There is a large financial incentive to minimise operations and maintenance (O&M) costs for offshore wind power by optimising the maintenance plan. The integration of condition monitoring (CM) and structural health monitoring (SHM) may help realise this. There is limited work on the integration of both CM and SHM for offshore wind power or the use of imperfectly operating monitoring equipment. In order to investigate this, a dynamic Bayesian network and limit state equations are coupled with Monte Carlo simulations to deteriorate components in a wind farm. The CM system has a ‘deterioration window’ allowing for the possible detection of faults up to 6 months in advance. The SHM system model uses a reduction in the probability of failure factor to account for lower modelling uncertainties. A case study is produced that shows a reduction in operating costs and also a reduction in risk. The lifetime levelised costs are reduced by approximately 6%.
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