A Generalized Framework for Digital Technologies for Fixed Foundation Offshore Wind Applications
Authors: Walker William P. , Fox J. Curtiss 
Published: May 2024

With the recent surge in installed offshore wind capacity around the world and the ever-increasing global aspirations for increasing offshore wind deployment, there are numerous efforts focusing on reducing the levelized cost of energy (LCOE) for offshore wind systems, mainly concentrating on reduction of the upfront capital expenditures. In contrast, this paper will explore a generalized framework to support advanced digital technology applications, such as structural health monitoring (SHM) and condition-based maintenance (CBM), with respect to the operations and maintenance of offshore wind assets to sustain the expected LCOE and increase the efficiency of operational expenditures. Specifically, this paper will focus on fixed bottom offshore wind installations and look to address additional requirements and challenges with floating offshore wind installations in a future publication.

This paper will review lessons learned from the offshore oil and gas and the electric power generation industries to demonstrate the relevance of a generalized framework for multiple operations and maintenance applications and to support the implementation of advanced digital applications. The key challenges to overcome in implementation strategies, such as data types and intervals, data governance policies, cyber-security considerations, and data management practices, will be illustrated through industry relevant examples and references to specific applications.

The fundamental asset classes for an offshore wind farm and advanced sensing technologies for them will be reviewed. Such assets include: the rotor nacelle assembly (RNA) including blades as separate assets, substructures and foundations, offshore substations, and cable systems including array and export cables. Parallels can be drawn to success in the oil and gas industry where the American Petroleum Institute (API) has developed integrity management recommended practices to support the operations and maintenance of risers, moorings, structures, etc.

The generalized framework will present a relational input and output bow-tie concept and outline specific industry needs to support implementation and broader industry adoption. The purpose of the generalized framework is to build the foundational understanding of the potential data types from each asset, interdependencies in data and information between assets, and information available within the data for decision making to enable the layering of higher level advanced digital applications on top of the framework to enable increased understanding of the asset’s performance and reliability.

Multiple digital technology applications will be reviewed, including applications of machine learning and artificial intelligence in support of information extraction, data consolidation and autonomous decision making, methods for sensor reduction through shared data and extrapolation, and the potential applications for digital twins within the offshore wind space. The paper will draw parallels and references to successes in other industries that have been able to develop common frameworks to achieve positive results.

Finally, the paper will address potential pathways forward through standardization efforts and industry collaboration to realize the benefits of common frameworks to support the offshore wind industry in the transition to large scale operations.