While low-level jets (LLJs) have been well characterised, their impact on offshore wind farms is not well understood. This study uses multi-elevation lidar scans to derive vertical wind profiles up to 350 m, detecting LLJs in up to 22.6 % of available measurements. Further, we analyse their effect on power conversion efficiency using operational wind farm data, observing a slightly negative influence and increased power fluctuations during LLJ events.

This editorial celebrates the 10th anniversary of Wind Energy Science, reflecting on a decade of rapid scientific progress and the journal’s role in advancing fundamental, interdisciplinary research. It highlights key developments in wind energy, the importance of open science and academia–industry collaboration, and emerging challenges such as data sharing and artificial intelligence. Above all, it honors the research community that has shaped the journal and looks ahead to the next decade.

This paper develops a nonlinear optimization framework to operate offshore energy islands with hybrid AC/DC grids, energy storage, and hydrogen production. Using realistic wind forecasts, uncertainty analysis and a detailed model inspired by the Princess Elisabeth Energy Island, the results show how the proposed approach can efficiently coordinate power flows and energy conversion systems, supporting the reliable and flexible integration of offshore wind energy.

We formulate and demonstrate a new digital shadow (i.e., a virtual copy) for wind turbines. The digital shadow is designed in order to be capable of mirroring the response of the machine even in complex inflow conditions. Results from field measurements illustrate the ability of the shadow to estimate loads with good accuracy, even with minimal tuning.

Weather effects drive wind turbine loads and performance to be different from those under mean atmospheric conditions. However, the influence of unsteady atmospheric phenomena on wake behavior remains unclear. This paper explores how atmospheric gravity waves – large-scale wave-like patterns caused by topographical features – affect meandering motions and turbulence generation in the wake region. The outputs of this paper can be used to guide wake modeling in realistic atmospheric flows.

This paper presents a method for designing control specifications based on mechanical fatigue. The method is validated by the design of controllers for a 15 MW reference wind turbine. The error in the fatigue estimation is smaller than 2 %, and the number of iterations during the controller design process has been significantly reduced.

The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability in winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.

This study proposes a random-forest-based machine learning (ML) model for fatigue life prediction of offshore wind turbine monopile foundations compared to traditional approaches, using long-term strain data from turbines in the Belgian North Sea. This study shows that the ML model predicts the fatigue life of monopile foundations more reliably when only short-term measurements are available, whereas for longer monitoring periods of greater than 12 months, simpler binning methods perform equally well.