Edward Hart

As global wind capacity expands, reducing operations and maintenance costs is critical to lowering the levelized cost of energy. This paper explores the state of the art in condition monitoring and prognostic strategies for wind turbine drivetrains, which are among the most failure-prone and maintenance-intensive subsystems. Current diagnostic methodologies are evaluated, covering supervisory control and data acquisition (SCADA) data, high-frequency vibration and acoustic analysis, machine learning and digital twin frameworks. Finally, practical challenges are identified that limit wide-scale industrial adoption, in order to guide future research and industrial efforts.

This paper studies the impacts of wake impingement on main bearing rating lives predicted during the wind turbine design stage. A computational tool chain was developed to explore and quantify these effects across a wind farm populated by 10 MW wind turbines. Wind field and turbine load modelling was undertaken using the Dynamiks Python package, including application of a dynamic wake meandering model. The ISO 281 basic bearing rating life formulation was subsequently applied in order to evaluate impacts from wake effects. Analyses included a two-turbine parametric analysis, followed by a full wind farm analysis undertaken for the TotalControl 32-turbine reference wind farm, including full wind rose simulations across all operational wind speeds. Site conditions were accounted for using a Weibull wind speed distribution and a range of parametric wind direction rose models. Results indicate that wind farm main bearing rating lives are negatively impacted by the effects of wake impingement, resulting in rating life reductions for the analysed wind farm of the order of 16 % on average and as much as 20 %–25 %, both for the locating main bearing. Despite these high sensitivities, it is important to note that these resultant rating lives (i.e. the predicted lives) still far exceed the standard wind turbine operational lifetimes of 20–30 years. Wake impacts were also found to be asymmetrically related to the side on which the rotor is impinged, suggesting that, for the main bearing, there may be a “better” side for wake impingement to occur. Rating life sensitivities to wind rose shape were also observed. While these findings must be interpreted with due consideration for the various methodological limitations present, they provide compelling evidence that wake effects at the wind farm level should necessarily be included when undertaking main bearing operational load modelling, rating life assessment, or other load-related analyses.

As wind energy scales up to meet global decarbonization and energy security goals, reducing the LCoE has become essential, particularly through improvements in O&M. This positioning paper explores the state-of-the-art in condition monitoring for wind turbines. It focuses on drivetrain components, which are among the most failure-prone and maintenance-intensive subsystems. It examines current diagnostic and prognostic strategies using SCADA data, vibration and acoustic analysis, and digital twin frameworks, alongside emerging techniques in machine learning, signal processing, and hybrid modelling. The paper also identifies key challenges, including data availability, labelling, standardization, and the gap between academic research and industrial adoption. This work aims to guide future research and industrial efforts in making wind energy more reliable, predictable, and cost-efficient.

Large offshore wind farms face operational challenges due to turbine wakes, which can reduce energy yield and increase structural fatigue. These problems may be mitigated through wind farm flow control techniques, which require reliable wake detection (recognising the presence of a clear wake) and characterisation (parametric description of a wake’s properties) as prerequisites. This paper presents a novel three-stage framework for generalised wake detection and characterisation. First, a regression model utilises blade loads, pitch and rotor rotational speed data to estimate the wind speed distribution across the rotor plane. Second, a convolutional neural network undertakes pattern recognition analysis to perform wake detection, classifying rotor-plane wind estimates as “fully impinged”, “left impinged”, “right impinged” or “not impinged”. Third, where wake impingement is detected, two-dimensional Gaussian fitting is undertaken to provide a parametric wake characterisation, providing outputs of the wake centre location and wake lateral width. The framework is trained and tested in a simulation environment incorporating the Mann turbulence model, the dynamic wake meandering (DWM) model for generating wakes and an industrial-grade aeroelastic solver. The testing is undertaken under a wide range of wind conditions, with mean ambient wind speeds from 5–15 m s⁻¹, turbulence intensities from 3 %–9 % and a full range of wind directions. Results show high accuracy of wind field estimation, with the mean RMSE across all test cases being 3.7 % when normalised by mean ambient wind speed. The wake detection model accurately identifies the presence of a wake for approximately 77 % of tested wind fields, with subpar performance observed for more extreme conditions or those at the limits of the training data used. The final wake characterisation stage is shown to flexibly adapt to changing wind conditions, successfully tracking the wake's position even for more turbulent conditions. The proposed framework therefore demonstrates strong potential as a generalised approach to wake detection and characterisation.

This brief communication presents a parametric model for the wind direction rose, based on ellipse geometry. Such a model supports standardisation and identification of generally representative cases, while also enabling systematic analyses of wind rose “shape” impacts on the benefits of proposed wind farm design and control innovations. Formulations include analytical wind direction rose modelling, model fitting to measured data via gradient descent minimisation of sum-of-square errors, and goodness-of-fit measures. Testing on wind direction data from real offshore wind farms confirms good performance, indicating that this parametric model is useful to wind energy research and development efforts. Possible model extensions are also discussed, including their benefits and drawbacks.

This paper studies the impacts of wake impingement on main bearing rating lives. A computational tool chain was developed to explore and quantify these effects across a wind farm populated by 10 MW wind turbines. Wind field and turbine load modelling was undertaken using the Dynamiks Python package, including application of a dynamic wake meandering model. The ISO 281 basic bearing rating life formulation was subsequently applied in order to evaluate impacts from wake effects. Analyses included a two-turbine parametric analysis, followed by a full wind farm analysis undertaken for the TotalControl 32-turbine reference wind farm, including full wind rose simulations across all operational wind speeds. Site conditions were accounted for using a Weibull wind speed distribution and a range of parametric wind direction rose models. Results indicate that wind farm main bearing rating lives are negatively impacted by the effects of wake impingement, resulting in rating life reductions for the analysed wind farm on the order of 16 % on average and as much as 20–25 %, both for the locating main bearing. Despite these high sensitivities, it is important to note that the resultant rating lives still far exceed the standard wind turbine operational lifetimes of 20–30 years. Wake impacts were also found to be asymmetrically related to the side on which the rotor is impinged, suggesting that, for the main bearing, there may be a "better" side for wake impingement to occur. Rating life sensitivities to wind rose shape were also observed. While these findings must be interpreted with due consideration for the various methodological limitations present, they provide compelling evidence that wake effects at the wind farm level should necessarily be included when undertaking main bearing operational load modelling, rating life assessment, or other load-related analyses.

Large offshore wind farms face operational challenges due to turbine wakes, which can reduce energy yield and increase structural fatigue. These problems may be mitigated through wind farm flow control techniques, which require reliable wake detection (recognising the presence of a clear wake) and characterisation (parametric description of a wake’s properties) as prerequisites. This paper presents a novel three-stage framework for generalised wake detection and characterisation. First, a regression model utilises blade loads and SCADA data to estimate the wind speed distribution across the rotor plane. Second, a Convolutional Neural Network (CNN) undertakes pattern recognition analysis to perform the wake detection, classifying rotor-plane wind estimates as "fully-impinged", “left-impinged”, “right-impinged” or “not impinged.” Third, where wake impingement is detected, 2D Gaussian fitting is undertaken to provide a parametric wake characterisation, providing outputs of the wake centre location and wake lateral width. The framework is tested and assessed using a virtual wind farm in the North Sea and a wide range of wind conditions (mean ambient wind speeds from 5–15 m/s, turbulence intensities from 3–9 %, full range of wind directions). Results show high accuracy of wind field estimation, with the mean RMSE over all test cases being 0.351 m/s, or 5.23 % when normalised by mean ambient wind speed. A wake detection sensitivity study confirms accurate performance across a majority of wind conditions, with minor issues observed only for more extreme conditions or those at the limits of the utilised training data. The final wake characterisation stage is shown to flexibly adapt to changing wind conditions, successfully tracking the wake’s position even in more demanding partial-impingement cases. The proposed framework therefore demonstrates strong potential as a generalised approach to wake detection and characterisation.

This brief communication presents a parametric model for the wind direction rose, based on ellipse geometry. Such a model supports standardisation and identification of generally representative cases, while also enabling systematic analyses of wind rose “shape” impacts on the benefits of proposed wind farm design and control innovations. Formulations include analytical wind direction rose modelling, model fitting to measured data via gradient descent minimisation of sum-of-square errors, and goodness-of-fit measures. Testing on wind direction data from real offshore wind farms confirms good performance, indicating this parametric model is useful to wind energy research and development efforts.