For modern wind turbines, the effects of inflow wind fluctuations on loads are becoming increasingly critical. Based on field measurements and simulations, we identify “bump” events responsible for high damage equivalent loads. In this article, we introduce a new characteristic of a wind field: the virtual center of wind pressure, which highly correlates to the identified load events observed in the operational measured data.

As wind turbines grow to unprecedented sizes, ensuring their reliability requires advanced testing. We reviewed the current state of wind tunnel experiments, examining how scaled physical models are evaluated for wind flow, flexibility, and noise. We conclude that while laboratory testing remains essential, it is most powerful when integrated with computer simulations and real-world data. This combined approach will ultimately drive the design of more efficient and sustainable wind energy.

Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.

Various simulation tools exist to provide load forecasts in the wind energy field (engineering models and numerical simulations). A newly introduced concept is the center of wind pressure, a quantity extracted from a wind field. In previous works, similar behaviour, then the main shaft bending moments, was shown. However, a clear relationship is missing. In this work, this gap is filled through the introduction of a calibration parameter.

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.

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.

Unexpected load events measured on operating wind turbines are not accurately predicted by numerical simulations. We introduce the periods of constant wind speed as a possible cause of such events. We measure and characterize their statistics from atmospheric data. Further comparisons to standard modelled data and experimental turbulence data suggest that such events are not intrinsic to small-scale turbulence and are not accurately described by current standard wind models.

We proof the dynamic inflow effect due to gusts in wind tunnel experiments with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg, where we created coherent gusts with an active grid. The effect is isolated in loads and rotor flow by comparison of a quasi-steady and a dynamic case. The observed effect is not caught by common dynamic inflow engineering models. An improvement to the Øye dynamic inflow model is proposed, matching experiment and corresponding FVWM simulations.

The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.

Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.

Wind turbines are often clustered within wind farms. A consequence is that some wind turbines may be exposed to the wakes of other turbines, which reduces their lifetime due to the wake turbulence. Knowledge of the wake is thus important, and we carried out wind tunnel experiments to investigate the wakes. We show how models that describe wakes of bluff bodies can help to improve the understanding of wind turbine wakes and wind turbine wake models, particularly by including a virtual origin.