This study demonstrates how kites used to generate wind energy can act as sensors to measure wind conditions and system behaviour. By combining data from existing sensors, such as those measuring position, speed, and forces on the tether, a sensor fusion technique accurately estimates wind conditions and kite performance. This approach can be integrated into control systems to help optimize energy generation and enhance the reliability of these systems under changing wind conditions.

Wind energy use has been rapidly expanding worldwide in recent years. Driven by global decarbonization goals and energy security concerns, this growth is expected to continue. To achieve these targets, production costs must decrease, with operation and maintenance being major contributors. This paper reviews current and emerging technologies for monitoring wind turbine drivetrains and highlights key academic and industrial challenges that may hinder progress.

Our study explores how frontal low-level jets (FLLJs) impact wind power production by causing ramp-down events. Using the Weather Research and Forecasting model, we analyzed various modeling configurations and found that initial and boundary conditions, domain configuration, and wind farm parameterization significantly influence simulations. Our findings show such extreme events can be forecasted 1 d in advance, helping manage wind power more efficiently for a stable, reliable energy supply.

To guide realistic atmospheric gravity wave simulations, we study flow over a two-dimensional hill and through a wind farm canopy, examining optimal domain size and damping layer setup. Wave properties based on non-dimensional numbers determine the optimal domain and damping parameters. Accurate solutions require the domain length to exceed the effective horizontal wavelength, height, and damping thickness to equal the vertical wavelength and non-dimensional damping strength between 1 and 10.

This paper proposes a data-driven approach to simulate wind turbine sensor time series, such as temperature and pressure signals, describing the behaviour of a wind turbine component as it degrades through time up to the failure point. It allows for the simulation of new failure events or the replication of a given failure under different conditions. The results show that the synthetic signals generated using this approach improve the performance of fault detection and prognosis methods.