Electricity usage in the United States doubled from 2.0 trillion to 4.0 trillion KWh between 1982 and 2022. Although demand has plateaued recently, projections suggest U.S. electricity consumption will continue to increase alongside demands for alternate energy resources. Offshore wind technology may bridge the gap between current and future energy needs. Existing fixed-bottom farms produce about half the energy that floating offshore farms do. With a single floating turbine producing 78 million KWh of electricity annually (enough to power 25,000 homes), floating offshore turbines offer a promising solution. To provide farms with maximum productivity, the National Renewable Energy Laboratory (NREL) is designing floating offshore farms with minimal malfunctions (see more about NREL's steps toward floating offshore wind energy here).

The goal of this project was to explore how failures in one turbine could affect the entire farm. We did this using a Bayesian network (i.e. a statistical approach to analyzing failure propagation). We formed the network using failure matrices and a modified breadth-first search and then calculated conditional probabilities for every failure in the farm. Our results found the average probability of failures spreading between turbines was 8.41%. To view the code for this project, please navigate to the FOWT Failure Analysis GitHub page.

We also applied this approach for failure propagation to the Floating Array Model (FAModel) software. The FAModel was designed by the floating offshore wind design team at NREL (more information about this profect found at NREL's website), and is meant to build a digital replica of floating offshore wind farms. In conjunction with the tools RAFT and MoorPy, this program takes into account farm design and site conditions to simulate farm behavior. More information about the FAModel and the open source code can be found on GitHub.