The Bureau of Ocean Energy Management and the Bureau of Safety and Environmental Enforcement have vast experience managing safety management systems for oil and gas projects in the Gulf of Mexico. Still, oil and gas protocols have not yet been adapted for commercial offshore wind projects in the United States.
The wind application team will engage with researchers, regulators and offshore wind developers on offshore wind safety management systems.
Team Leads
Wind application team leads are:
- Krish Sharman (University of Massachusetts)
- Walter Musial (National Renewable Energy Laboratory)
- Rebecca Green (National Renewable Energy Laboratory)
- MH Kim (Texas A&M University)
- Mario Rotea (University of Texas – Dallas)
Focus Areas
The team’s focus areas are:
- Safety management systems (SMS) for offshore wind energy: We will develop, deploy and assess technologies, methods and systems that facilitate the development of an SMS for offshore wind energy projects. The objectives are to:
- Ensure the safe installation, operation and maintenance of offshore wind energy systems throughout their lifetime.
- Reduce human exposure to hazardous activities during installation, operation and maintenance of offshore wind energy systems.
- Protect the natural environment during all stages of the development of a wind energy system.
- Met-ocean data gathering and analysis systems: We will design observational strategies to consider flow distortion effects and how estimates of local met-ocean conditions are influenced as a result. Offshore platform monitoring will provide an understanding of local disturbances to the flow due to moderate conditions to severe tropical storms and tropical cyclones, and customized met-ocean measurement programs in regions prone to extreme conditions will provide an opportunity to fill in data and knowledge gaps.
- Digital twins and machine learning for safe and revenue-optimized offshore wind operations: Digital twins will augment sparsely-sensored condition monitoring systems and will be integrated with machine learning-based methods in offshore wind turbine farms to provide effective fault diagnostics and identify under-performing turbines. Specifically, machine learning-assisted health monitoring will be applied to offshore wind turbines to reduce offshore inspection.
- Processes for safe installation, transfer and access for offshore wind systems and humans: A safer sequential installation method for large offshore turbines (15 megawatt) can be developed by using reliable multibody crane installation simulations. Quay-side installation and wet-towing approaches can be tested for large offshore turbine installation. The results from numerical simulations and experimental programs will be integrated into guidelines and easy-to-use tools that can be used by operators and regulators.
