Laboratory testing campaigns for the wind energy industry play an essential role in testing innovative control strategies and digital twin applications. But incidents during testing can be detrimental and might cause project delays and damage to expensive equipment. We propose an anomaly detection scheme for laboratory experiments that are developed and tested to enhance reaction time and prediction quality, reducing the likelihood of damage to equipment due to human error or software malfunction.

This study utilizes a method to analyze power conversion dynamics across different operational states, addressing non-stationarity with a correlation matrix algorithm. Findings reveal distinct dynamics for each state, emphasizing their impact on system behavior and offering a solution for hysteresis effects in power conversion dynamics.

In this article, we present the measurements of local aerodynamic sectional characteristics on a full-scale rotor blade with a novel add-on instrumentation comprising a wake rake, a pressure belt, and a five-hole Pitot tube. In general, the demonstration of this instrumentation opens a range of promising new options for optimizing airfoil sectional performance in its real operating environment, e.g., the size and position of vortex generators.

Wind turbines with swept blades have the potential to improve power production and reduce loads, but their actual benefits are uncertain, and they are difficult to analyse. We developed a simplified yet accurate aerodynamic model, coupling two engineering models, to predict their performance. Tests against high-fidelity simulations show that the method offers reliable results with low computational effort, making it ideal for load calculations and design optimization of swept blades.

We examined the power spectra of wind speed in three convection-permitting models in central Europe and found that these models have a better representation of wind variability characteristics than standard wind datasets like the New European Wind Atlas, due to different simulation approaches, providing more reliable extreme wind predictions.

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.

This paper introduces FastLE, a rapid load extrapolation method for wind turbines that complies with International Electrotechnical Commission (IEC) 61400-1 requirements. Employing a load distribution meta-model, FastLE reduces computation time from 20 h to less than 1 s, achieving the mean absolute percentage error of 3.165 %. It enables efficient wind farm optimization while ensuring structural integrity and safety.