This work employs deep neural networks to identify damage in the mooring system of a floating offshore wind turbine using measurements from the platform response. We account for the effect of uncertainty caused by the existence of multiple solutions using a Gaussian mixture model to describe the damage condition estimates. The results reveal the capability of the methodology to discover the uncertainty in the assessment, which increases as the instrumentation system becomes more limited.

Wind turbines add turbulence to the atmosphere behind them, especially 4–6 diameters downstream and near the rotor top. We propose an equation that predicts the distribution of added turbulence as a function of a turbine parameter (thrust coefficient) and an atmospheric parameter (undisturbed turbulence intensity before the turbine). We find that our equation performs well, although not perfectly. Ultimately this equation can be used to better understand how wind turbines affect the atmosphere.

Noise from wind turbines has been contentious for many years for those who have to live near wind farms. Despite strict regulations and significant research into the subject, it continues to be an issue for wind energy's role in the green transition. We were curious to find out why. Our research into three research ("epistemic") communities shows that noise is not understood as the same thing by the scientists dealing with it, fuelling the controversies over the solutions proposed.

Wind energy has been dominated by ever-increasing single-rotor wind turbines. Structural scaling laws make multi-rotor wind turbines attractive, as they can achieve similar power outputs but work with, rather than against, scaling laws. This work investigates high-level control strategies for a 23-rotor multi-rotor wind turbine, including the aerodynamic interactions between the rotors, and suggests an alternative to pitch control using multi-rotor furling.