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.

The emergence of inverter-based resources has brought significant changes to power system analysis. Nowadays, grid-forming control is seen as a solution to stabilize the system, but most of the studies consider a constant direct current bus. This paper introduces wind turbine dynamics and studies the interaction between wind turbine, grid forming, and the alternating current grid. Using a simplified model an undamped oscillation is highlighted and analyzed. To mitigate it, two filters are proposed and compared.

We introduce a new method to refine failure prediction for wind turbines, leading to better and more efficient alarming. We do this by filtering detected anomalies based on the anomalies from the whole fleet. We compare submethods and find one that removes up to 65 % of detected anomalies while leaving the failure-predicting ones. We also detail how we trained the model that generated these anomalies and discuss the construction of the scalable pipeline that was used to deploy such models.

Wind turbines in offshore lease areas (LAs) along the eastern US may be impacted by hurricanes. Regional simulations with atmosphere-only and coupled atmosphere–ocean–wave regional models reproduce historical hurricanes well and indicate no exceedance of 50 m s^-1 wind speeds in the LAs and only brief periods with low power production. Coupled simulations lead to more intense hurricanes, possibly indicating that previous atmosphere-only simulations underestimate the risk to offshore wind turbines.

We developed a detailed simulation to better understand how tethered aircraft can fly in wind to generate energy. By accurately modeling the aerodynamics around the aircraft and how the aircraft reacts and is controlled, we can gain new insight into how to improve efficiency and safety. Our virtual environment successfully followed a planned flight path and revealed potential design and operational improvements based on the analysis of the airflow.

This research presents an assessment of hurricane risk to offshore wind turbine systems in the Gulf of Mexico. Hurricanes that frequent this area can potentially exceed design limits prescribed by the International Electrotechnical Commission (IEC) wind design standards. Translations between the well-established Saffir–Simpson scale and the IEC design classes were developed to convert communication of hurricane severity into terms of design load conditions familiar to wind turbine designers.

We proposed a new method for active power control that uniquely combines induction control with wake steering to maximize power tracking margins. Our methodology results in significantly improved robustness against wind fluctuations and fatigue loading when compared to the state of the art.

Increased sizes of modern turbines require extended descriptions of the atmospheric wind and its correlation to loads. Here, a surrogate stochastic method for estimating the bending moments at the main shaft is proposed. Based on the center of wind pressure dynamics, an advantage is the possibility of stochastically reconstructing large amounts of load data. Atmospheric measurements and modeled data demonstrate the validity of this method.