We use a mesoscale numerical weather prediction model to conduct a case study of a thunderstorm outflow passing over and interacting with a wind farm. These simulations and observations from a nearby radar and surface station confirm that interactions with the wind farm cause the outflow to reduce its speed by over 20 km h⁻¹, with brief but significant impacts on the local meteorology, including temperature, moisture, and winds. Precipitation accumulation across the region was unaffected.

This paper considers the modelling of wind turbine main bearings using analytical models. The validity of simplified analytical representations is explored by comparing main-bearing force reactions with those obtained from higher-fidelity 3D finite-element models. Results indicate that good agreement can be achieved between the analytical and 3D models in the case of both non-moment-reacting (such as for a spherical roller bearing) and moment-reacting (such as a tapered roller bearing) set-ups.

This work describes a series of tests of active flaps on a 4 MW wind turbine. The measurements were performed between October 2017 and June 2019 using two different active flap configurations on a blade of the turbine, showing a potential to manipulate the loading of the turbine between 5 % and 10 %. This project is performed with the aim of demonstrating a technology with the potential of reducing the levelized cost of energy for wind power.

Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.

Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.

With the increase in installed wind capacity, the rotor diameter of wind turbines is becoming larger and larger, and therefore it is necessary to take aeroelasticity into consideration. At the same time, wind turbines are in reality subjected to atmospheric inflow leading to high wind instabilities and fluctuations. Within this work, a high-fidelity chain is used to analyze the effects of both by the use of models of the same turbine with increasing complexity and technical details.

Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.

This paper assesses the behaviour and causality of sudden variations in wind power generation over a short period of time, also called "ramp events". It is shown, amongst other things, that ramps at the study site are mostly associated with frontal activity. Overall, the research contributes to a better understanding of the drivers and behaviours of wind power ramps at the wind farm scale, beneficial to ramp forecasting and ramp modelling.

This project is a comparative study that takes into consideration various airfoils from the Selig, NACA, and Eppler families and models them as diffusers of the wind turbine. The efficiency of the diffuser-augmented wind turbine can be enhanced by optimizing the geometry of the diffuser shape. Their subsequent performance trends were then analyzed, and the lower-performing airfoils were systematically eliminated to leave us with an optimum design.

This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.

Revenues from the operation of wind turbines in Germany will be insecure in the future due to the expiration of federal support. Alternative ways of selling electricity are usually based on exchange prices. Therefore, the long-term revenue potential of wind turbines is assessed based on levelized revenue of energy (LROE), using a new forecasting model and open-source data. Results show how different expansion scenarios and emission prices may affect profitability of future plants.

This paper describes a new analysis of wind turbine thrust based on removing pressure from the equations for the wind flow through a wind turbine rotor. We show that the equation is free from the effects of flow expansion that must accompany the slowing down of the wind through the blades as they extract the kinetic energy. The conditions under which the assumptions are used in blade-element analysis, which is fundamental for wind turbine aerodynamics, are made clear for the first time.

This article regards a rotor redesign for a wind turbine in upwind and in downwind rotor configurations. A simple optimization tool is used to estimate the aerodynamic planform, as well as the structural mass distribution of the rotor blade. The designs are evaluated in full load base calculations according to the IEC standard with the aeroelastic tool HAWC2. A scaling model is used to scale turbine and energy costs from the design loads and compare the costs for the turbine configurations.

This paper presents two methods on how to estimate the lift force that is created by a wing. These methods were experimentally assessed in a wind tunnel. Furthermore, an active trailing-edge flap, as seen on airplanes for example, is used to alleviate fluctuating loads that are created within the employed wind tunnel. Thereby, an active flow control device that can potentially serve on wind turbines to lower fatigue or lower the material used for the blades is examined.

This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.

The paper proposes a quantitative, non-probabilistic metric for the preliminary comparison of safety of windfarm service operation vessels (SOV) in typical phases of operation. The metric is used as a conditional proxy for the incident likelihood, conditioned upon the presence of similar resources (manpower, time, skills, knowledge, information, etc.) for risk management across compared operational phases.

The article describes results of experimental wind tunnel and CFD testing of four different straight-bladed vertical axis wind turbine model configurations. The experiment tested a novel concept of vertically dividing and azimuthally shifting a turbine rotor into two parts with a specific uneven height division in order to limit cycle amplitudes and average cycle values of bending moments at the bottom of the turbine shaft to increase product lifetime, especially for industrial-scale turbines.

Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.