Multivariate analysis of the reliability, availability, and maintainability characterizations of a Spar–Buoy wave energy converter farm

This is the final version of the article. Available from Springer via the DOI in this record.Quantitative reliability, availability, and maintainability (RAM) assessments are of fundamental importance at the early design stages, as well as planning and operation of marine renewable energy systems. This paper presents an RAM framework adaptable to different offshore renewable technologies, conceived to provide support in the choice of the device components and subsequent planning of the O&M strategies. A case study, characterizing a pilot farm of oscillating water column (OWC) wave energy converters (WECs), is illustrated together with the method used to obtain reliable estimate of its key performance indicators (KPIs). Based on a fixed feed-in-tariff for the project, economic figures are estimated, showing a direct relationship with the availability of the farm and the cost of maintenance interventions. Consequently, the probability distributions of the most relevant output variables are presented, and the mutual correlations between them investigated using principal components analysis (PCA) with the aim of discovering the relationships influencing the performance of the offshore farm. In this way, the contributions of the individual factors on the profitability of the project are quantified, and generic guidelines to support the decision-making process are derived. It is shown how this type of analysis provides important insights not only to ocean energy farm operators after the deployment of the devices, but also to device developers at the early design stage of wave energy concepts.The first and second authors were partially funded by the Marie Curie Actions of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement number 607656 (OceaNet project). The fourth author was funded by FCT researcher grant No. IF/01457/2014. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 654444 (OPERA Project) and from the FCT project PTDC/MAR-TEC/0914/2014