Power converter faults in wind turbines often lead to costly downtime and repeated maintenance. We present a practical, explainable, and fully data-driven approach that utilizes high-resolution converter control system records, 1 min operating data, and event logs to predict whether a fault leads to a long or short standstill. By combining engineered features with interpretable feature reduction, we achieve 89 % accuracy and an F1 score of 0.86, providing support for remote decision-making.

Vortex generators (VGs) are being used to prevent flow separation and stall on wind turbine blades. Optimal VG designs are chosen from steady investigations, assuming similar separation control characteristics between steady and unsteady conditions. Surface pressure measurements on a thick pitching airfoil with VGs show that VGs larger than the optimal steady size and rectangular VGs instead of the common triangular VGs must be used to consistently prevent unsteady flow separation and dynamic stall.

We formulate a controller for wind turbines that has three main characteristics. First, it optimizes profit by balancing revenue from power generation with cost. Second, cost includes the effects of cyclic fatigue that, departing from most of the existing literature on control, is rigorously accounted for by an exact cycle counting on receding horizons. Third, it uses a model capable of learning and improving its performance based on measured or synthetic data.

Mesoscale simulations with the Fitch wind farm parameterization were compared to large-domain large-eddy simulations for three planned offshore wind farms under varied atmospheric conditions. Mesoscale runs captured key wake deficit patterns and stability effects in the wind farm wake evolution but underestimated power losses from internal wakes, especially in aligned winds or stable conditions. Results highlight mesoscale strengths for large-scale wakes and limits for turbine-level losses.

Small wind turbines could help to generate renewable energy but are often inefficient and expensive. This study tested a counter-rotating dual-rotor design with both rotors connected through one generator, eliminating gearboxes. Wind tunnel tests achieved a 1 kW output with 33 % efficiency. A computational model showed that the turbine could theoretically reach 56 % efficiency with optimized blade angles. This compact design reduces mechanical complexity and suits urban applications.

Offshore wind turbines are prone to fatigue caused by wind, wave, and operational loads, and lifetime extension may be enabled by monitoring stress histories. However, this is challenging because parts of the structure are sub‑sea and sub‑soil parts. Model‑based virtual sensing offers a solution, but current models simplify the rotor, which can lead to errors. This work addresses these errors and concludes that accuracy may be improved by including a flexible-rotor model and environmental variability.

A new method to synthesize a wind field with very low error with respect to the theoretical, expected statistics is presented. The presented method has been developed with the aim of validating the effectiveness of a wind reconstruction algorithm in the context of a gust load alleviation project. Finally, the proposed method allows us to synthesize Gaussian and stationary phenomena with high accuracy in various spatial domains with uniformly spaced rectangular grid shapes.

Wind energy use has been rapidly expanding worldwide in recent years. Driven by global decarbonization goals and energy security concerns, this growth is expected to continue. To achieve these targets, production costs must decrease, with operation and maintenance being major contributors. This paper reviews current and emerging technologies for monitoring wind turbine drivetrains, and highlights key academic and industrial challenges that may hinder progress.

This study investigated coastal wind conditions in Japan using year-long light detection and ranging observations from onshore and offshore sites, 1.6 km apart. The wind speed profiles differed significantly. Low-level jets (LLJs) occurred more frequently in specific seasons and wind directions, and their timing varied with direction. These findings indicate a strong link between land and sea breeze circulation and LLJ formation and demonstrate the need for further Japan-specific evaluations.

This work investigates the impact of wind direction uncertainty on wake steering, a promising flow-control strategy that aims to increase the efficiency of wind farms, using high-fidelity computational fluid dynamics. It concludes that wake steering is sensitive to both bias and uncertainty in inflow wind direction due to having a relatively small range over which gains are predicted and showing significant decreases in peak power output with increasing wind direction uncertainty.

We introduce fast response methods to predict how floating wind turbines behave in early design stages. By transforming the equations of motion into a form that is easier to compute, our approach avoids longer simulations while preserving accuracy. We developed both single and double perturbation methods, which run far faster than standard models with errors under 3.5 %. The single perturbation method at second order offers the strongest balance of speed and accuracy.

We propose a machine learning model that represents wind turbines as nodes in a graph, learning how they interact to predict the wind speed at any location on a farm. Trained on thousands of simulated farms, it captures complex wake interactions across varying layouts and sizes at low computational cost, supporting faster neighboring farm wake assessment.