This study analyses maintenance data from over 1000 onshore and offshore wind turbines, covering 4200 operating years, to assess failure rates, repair times, and maintenance needs. It compares failure rates per turbine and per megawatt, examines time-dependent failure behaviour, and evaluates maintenance interventions. Results show higher onshore failure rates and identify the pitch, control, and converter systems as most critical.

Our analysis has revealed that the natural frequencies of the tower vary depending on the operational state of the turbine and are significantly influenced by the flexibility of the support structure. For a spar-type floater, a mismatch of 40 % was found. This work highlights the importance of continuous structural monitoring of offshore assets, the need to raise awareness within the design community and the current lack of explicit guidance on this issue in recommended industry practices.

This study presents continuous lifetime assessments of wind turbine structural (tower) and rotating components (main bearings) over nearly a decade, combining controller data, calibrated load measurements, and a virtual load sensor at the tower bottom. The components' estimated lifetimes exceeded the design lifetime. Contrary to expectations, lower turbulence intensity at rated wind speed increased fatigue loads of the locating main bearing due to turbulence averaging.

This work presents the scaled experimental validation of an active power control (APC) algorithm that improves wind farm power-tracking accuracy during turbulent wind lulls. Tests in a large low-blockage wind tunnel, including dynamic wind-direction changes, show that the method outperforms three reference APC strategies, especially under high power demand, while keeping structural fatigue low.

This large-eddy simulation study identifies a realistic set-up for modeling wind-farm–atmosphere interactions, validates a method that minimizes non-physical gravity waves, and examines how real gravity waves and wind farm performance depend on non-dimensional parameters defining atmospheric stability and farm geometry. The results show how realistic and accurate modeling are critical to performance prediction.

Characterizing the wind resource is much more than just measuring wind speeds. In fact, the physics of the atmosphere is governed by a complex interplay of different quantities, temperature being one of the most important. We used a new technology to remotely sense temperature profiles around wind farms at AWAKEN (American WAKE ExperimeNt). Here, we discuss the methodology and guide readers through a comprehensive, step-by-step validation effort to quantify the accuracy of temperature profiling for wind energy.

Wake steering is a promising strategy for wind farm optimization, but its success hinges on accurate wake models. We assess models of varying fidelity for a 22 MW reference turbine, comparing single- and two-turbine cases against large-eddy simulations. All models reproduced qualitative trends for power and loads (if applicable), but quantitative agreement varied, and in general the error increased with increasing yaw angle.

Large wind turbines are highly sensitive to changing winds, yet current measurements miss important details. This study compares three methods to reconstruct the full wind field ahead of a turbine in real time using lidar data and simulations. The results show these approaches can capture detailed inflow structures, which could help turbines anticipate wind changes, improve control strategies, and reduce structural loads.

This paper investigates a novel drivetrain concept for wind turbines. The proposed approach allows for the elimination of the frequency converter that is typically required to enable variable-rotor speed. Instead, variable-speed operation is realized through a combination of hydraulic and mechanical transmission. The concept can achieve efficiency levels comparable to conventional drivetrains while avoiding the high susceptibility to faults and costs associated with frequency converters.

The performance of high-performance crosswind kite systems is strongly affected by the complex wake structures they generate. We develop an unsteady, vortex-based wake model that can be efficiently integrated into optimal control frameworks for flight trajectory optimization. The model is shown to provide good agreement with higher-fidelity simulations while incurring only moderate additional computational cost, enabling more reliable performance prediction.

This paper presents a framework to identify wind turbine noise in long-term field measurements. By combining statistical criteria, turbine operating data, and physics-based signal analysis, periods dominated by wind turbine noise and its key components are detected. The framework is validated using a structured listening test and applied to a 1-month dataset. The framework, listening-test platform, and anonymized audio data are publicly available to support further research.

This work introduces a novel method to detect and quantify uniform pitch misalignment in wind turbines. This fault, where all blades are equally misaligned, is hard to detect because it causes no immediate imbalance but reduces efficiency over time. By combining physics-based features with machine learning techniques, our approach reliably identifies and quantifies this fault under various wind regime conditions, improving turbine maintenance and energy production.

Using data collected over 13 months at an offshore wind farm, our study shows that a wind turbine’s position within the farm influences its energy output at a given nacelle-measured wind speed. Front-row turbines respond differently to similar wind speeds and turbulence than those further back. This finding suggests that current methods for characterizing inflow conditions may not fully capture actual wind behavior, underscoring the need for improved performance analysis techniques.

This study explores how the movement of floating wind turbines affects nearby turbines through wakes. Using wind tunnel experiments, we found that certain motions of an upstream turbine can improve the energy produced by a downstream one and change the forces it experiences. These effects depend on how the turbines are spaced and aligned. Our results show that the motion of floating turbines plays a key role in how future offshore wind farms should be designed and operated.

This study delivers a method and datasets for a global offshore atlas for turbulence intensity from a height of 10 m to 200 m. The method innovatively includes both two-dimensional and three-dimensional turbulence, along with stability, wave age and height. Results show satisfactory agreement with measurements and data from the literature.

In wind farms, upstream turbine wakes negatively influence performance of downstream turbines. To mitigate this, we optimized wind farm performance by directly controlling generated wakes and using them as feedback. We used a light detection and ranging (lidar) device to track generated airflow and adjust upstream turbines in real time. This approach increased overall power output while keeping additional structural loading on the turbines low.

This study presents a method to estimate wind speeds that could occur in a 50-year period. The 50-year wind speed is calculated for three regions: Taiwan, Japan, and the east coast of the US. The method performs well in Taiwan and Japan, which can be attributed to the large dataset size located in a limited spatial area. The east coast of the US performs less well due to the smaller dataset size and wider spatial region that they cover.

We studied how different industry-oriented computer models predict the behavior of winds behind turbines in a wind farm. These wakes reduce energy output and can affect turbines further down the row. By comparing these three models with more detailed simulations, we found they agree well on overall power but differ in how they capture turbulence and wear on machines. Our results show where the models need improvement to make wind farm computer models more accurate and reliable in the future.

To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States east coast for 18 months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.