Fraunhofer Institute for Wind Energy Systems

Wind energy research relies on remote sensing technologies like dual-Doppler (DD) lidar and radar, or space-born SAR data to estimate complex meteorological conditions and the flow field around wind farms. Offshore measurement campaigns over the last decade accentuate the potential of DD wind radar technology for wind energy application. Onshore, promising results were also reported as part of the American WAKE ExperimeNt (AWAKEN).The main drawback of wind radar technology is that further characterisation of radar measurement accuracy is required for industry implementation. We investigate X-band wind radar measurement accuracy in a comparison study, using meteorological mast data and wind lidars from an onshore measurement campaign. Despite their cost, meteorological masts provide very accurate point measurements over specific heights, and are the preferred reference for wind validation. Wind lidars are a proven alternative, as demonstrated in several offshore campaigns. Like for lidars, radar's measurement principle is based on the Doppler effect, and both devices allow for wind field estimates. Yet, radars can scan over larger distances at a much higher sampling rate, with increased resolution along the beam due to the use of a compressed pulse. However, unlike pulsed lidars that emit collimated beams, the radar beam expands with distance, which arguably adds uncertainty to the measurements at far ranges. A dual-Doppler lidar-radar set-up is used with the remote sensing devices collocated in space, but measuring a-synchronously. We conduct the analysis on wind component basis (u, v) and wind speed and direction, focusing on inflow in front of a large wind turbine (OPUS 1) at the onshore Wivaldi Test Site in Northern Germany, as part of the radar Krummendeich Campaign. Lidars and radars are deployed approximately 3 km to 4 km away from the collocated lidar - meteorological mast - radar measurements. Although the influence of increased probe volume averaging, due to beam expansion, in distance is unclear due to campaign set-up, the results presented set a ground base for further use of long range wind lidars as validation for upcoming radar measurement campaigns.

The increasing integration of converter-based technologies is reshaping power system dynamics and placing new demands on electrical simulation development and practices. Accurate modelling of these fast-acting control systems is essential for system stability studies, yet current model sharing practices face persistent challenges in terms of interoperability, maintainability, and confidentiality—especially in multi-vendor environments which are becoming increasingly frequent across renewable projects. This paper discusses the need for standardized, tool-independent simulation interfaces, focusing on a promising standard interface solution and presenting the main outcome of its implementation, in terms of challenges and benefits. It also outlines key recommendations for future development aiming to support ongoing standardization efforts and guide the work of future development groups toward more robust, scalable, and user-friendly offline and real-time capable simulation frameworks.

<strong class="journal-contentHeaderColor">Abstract.</strong> This paper presents a decision support system that integrates digital twin technology with advanced economic evaluation tools to enable continuous and holistic assessment of wind farm investments. The framework combines real-time turbine health data, including remaining useful life estimates derived from SCADA and condition monitoring systems, with financial models employing discounted cash flow, scenario testing, Monte Carlo simulations, and real-options valuation. Implemented through the open-source DigiWind platform, the system adheres to FAIR data principles and provides a flexible, interoperable environment for asset management. A case study on an 8 MW wind turbine in Germany demonstrates the framework&rsquo;s ability to guide decisions such as life extension, repowering, decommissioning, or sale under volatile market conditions. Results highlight the importance of coupling technical reliability forecasts with market-based financial outlooks to capture both risks and upside potential, offering a scalable and transparent tool for investors, operators, and policymakers navigating the evolving wind energy sector.

<strong class="journal-contentHeaderColor">Abstract.</strong> This study explores the aeroelastic behavior of the IEA 15 MW wind turbine rotor during dynamic yaw maneuvers under storm conditions through high-fidelity computational fluid dynamics (CFD) simulations. The focus is on blade vibration responses to a variety of yaw misalignments while maintaining constant pitch and azimuth settings. Utilizing the Geometrically Exact Beam Theory (GEBT) and the OpenFOAM framework, the study reveals that certain yaw angles lead to significant edgewise blade vibrations, with distinct responses observed among the rotor's three blades. Detailed analysis of one blade at varying fixed yaw angles, employing Hilbert-Huang transformation, uncovers a lock-in effect where flow structures synchronize with the blade's eigenfrequencies, resulting in pronounced blade tip vibrations. Key findings indicate that the most substantial vibrations occur during specific yaw angles, suggesting that the rotor's structural integrity could be compromised under certain dynamic conditions. This work enhances the understanding of aeroelastic instabilities during off-design yaw maneuvers and highlights the need for operational strategies in managing rotor performance during extreme conditions.

Abstract. Large rolling bearings with complex interfaces need reliable finite-element (FE) models to determine the load distribution and deformation behavior. To ensure the accuracy of the results, it is important to validate the models against experimental data. Several works on models with different approaches are published, but rarely is this validated. The present work firstly validates a finite-element model of an original size three-row roller wind turbine blade bearing. For the validation, strain gauges and laser sensors are used to compare the deformation behavior and radial displacements of the bearing rings against experimental results. A characteristic of three-row roller bearings is the segmentation of one of the rings for manufacturing purposes. In this work, the authors investigate the influence of different coefficients of friction between the segmented outer ring and different bolt preloads on the occurring strain on the bearing rings. Two different sets of bolt preloads were considered: one to represent operational behavior with no relative movement between the segments of the split ring and one with gap opening and sliding to investigate nonlinear behavior of the bearing. The result of this work is a validated finite-element bearing and test rig model for different parameter sets and loads.

<strong class="journal-contentHeaderColor">Abstract.</strong> Large rolling bearings with complex interfaces need reliable finite-element models to determine the load distribution and deformation behavior. To ensure the accuracy of the results, it is important to validate the models against experimental data. Several works on models with different approaches are published but rarely is this validated. The present work now firstly validates a finite-element model of an original size three-row roller wind turbine blade bearing. For the validation, strain gauges are used to compare the deformation behavior of the bearing rings against experimental results. A characteristic of three-row roller bearings is the segmentation of one of the rings for manufacturing purposes. In this work, the authors investigate the influence of different coefficients of friction between the segmented outer ring and different bolt preloads on the occurring strain on the bearing rings. Two different sets of bolt preloads were considered: One to represent operational behavior with no relative movement between the segments of the split ring and one with gap opening and sliding to investigate nonlinear behavior of the bearing. The result of this work is a validated finite-element bearing and test rig model for different parameter sets and loads.

Abstract. This paper presents a decision support system that integrates digital twin technology with advanced economic evaluation tools to enable continuous and holistic assessment of wind farm investments. The framework combines real-time turbine health data, including remaining useful life estimates derived from SCADA and condition monitoring systems, with financial models employing discounted cash flow, scenario testing, Monte Carlo simulations, and real-options valuation. Implemented through the open-source DigiWind platform, the system adheres to FAIR data principles and provides a flexible, interoperable environment for asset management. A case study on an 8 MW wind turbine in Germany demonstrates the framework’s ability to guide decisions such as life extension, repowering, decommissioning, or sale under volatile market conditions. Results highlight the importance of coupling technical reliability forecasts with market-based financial outlooks to capture both risks and upside potential, offering a scalable and transparent tool for investors, operators, and policymakers navigating the evolving wind energy sector.

<strong class="journal-contentHeaderColor">Abstract.</strong> The expansion and creation of new wind farms in recent years brings up challenges to manage both inter- and intra- wind farm wake effects. Wake blockage impact heavily relies on atmospheric conditions for determining how long and how intense the wake propagation is. One of these key atmospheric parameters is the height of the atmospheric boundary layer (ABL), which determines the height of the atmosphere most impacted by surface layer wind speed and temperature regimes. Generally lower ABL is united with more stable conditions, and thus greater the wake propagation. Offshore wind farms experience more frequent stable conditions compared to onshore farms, thus boundary layer conditions must be well parameterized to accurately model wake blockage effects. Scanning lidars present a viable solution for boundary layer height determination. In this study, their measurements are compared against ERA5 and WRF ABL model outputs. The lidar acts as a reference for boundary layer conditions, categorizing the ABL for both the mixing (convectively driven) and residual (stably driven) layer heights. Two campaigns, both using WindCube Scan devices, were assessed: one located completely offshore and another on a coastline. The results demonstrate an overestimation of the boundary layer height from both ERA5 and WRF from the offshore site of around 400 m. The coastal site yielded mixed results when comparing the ABL model height to the mixing and residual layer heights derived from the lidar, with a model overestimation compared to the mixing height of 300 m and for the residual height of 750 m. A sensitivity study demonstrates the bias of both models correlates to the ABL diurnal cycle and to temperature flux misrepresentation in the model.

<strong class="journal-contentHeaderColor">Abstract.</strong> Power converters are among the most frequently failing subsystems of onshore and offshore wind turbines. In order to minimize the resulting downtime and production losses, the time to repair should be as low as possible. In practice, however, it is not uncommon for several turbine visits to be necessary, as information about the failure mode and the spare parts required can often only be determined on site. This paper presents a data-driven, interpretable workflow for the remote diagnosis of power-converter&ndash;related turbine shutdowns using converter control system data from an offshore wind farm. The study uses converter-fault events and three data sources: high-resolution fast logs (4.5 kHz, &minus;350 ms to +200 ms around a fault-induced trigger), 1-min operating data, and fault flags derived from event log data. From an initial 864 engineered features we remove low-variance and highly correlated features, apply a subsampled decision-tree inclusion-rate filter to retain 34 features, and estimate diagnostic impact via subsampled logistic regression. Results show that fast-log features and converter fault flags contain the most predictive information for classifying standstill severity after a fault-induced shutdown, while low-resolution operating data contribute little. Using four of the derived features yields the best cross-validated performance in a decision tree with an accuracy of 0.89 and an F1-score of 0.86. The proposed approach is practical for industry use and offers the potential to provide explainable decision support for improving first-time fix rate.

Abstract. The expansion and creation of new wind farms in recent years brings up challenges to manage both inter- and intra- wind farm wake effects. Wake blockage impact heavily relies on atmospheric conditions for determining how long and how intense the wake propagation is. One of these key atmospheric parameters is the height of the atmospheric boundary layer (ABL), which determines the height of the atmosphere most impacted by surface layer wind speed and temperature regimes. Generally lower ABL is united with more stable conditions, and thus greater the wake propagation. Offshore wind farms experience more frequent stable conditions compared to onshore farms, thus boundary layer conditions must be well parameterized to accurately model wake blockage effects. Scanning lidars present a viable solution for boundary layer height determination. In this study, their measurements are compared against ERA5 and WRF ABL model outputs. The lidar acts as a reference for boundary layer conditions, categorizing the ABL for both the mixing (convectively driven) and residual (stably driven) layer heights. Two campaigns, both using WindCube Scan devices, were assessed: one located completely offshore and another on a coastline. The results demonstrate an overestimation of the boundary layer height from both ERA5 and WRF from the offshore site of around 400 m. The coastal site yielded mixed results when comparing the ABL model height to the mixing and residual layer heights derived from the lidar, with a model overestimation compared to the mixing height of 300 m and for the residual height of 750 m. A sensitivity study demonstrates the bias of both models correlates to the ABL diurnal cycle and to temperature flux misrepresentation in the model.