Cavity-type defects on a wind turbine rotor blade due to erosion lead to an sub-optimal boundary layer flow behavior and result in a reduced annual energy production. To enable an indirect defect detection from far distances, with no contact and no wind turbine stop, the thermographic visualization of the defect wake flow is studied. Experimental investigations and complementary flow simulations on two different airfoil shapes show that the detection is possible above a critical Reynolds number. Furthermore, the wake flow depends on multiple influencing quantities such as the airfoil geometry and the associated chord Reynolds number as well as the cavity position, the cavity shape and the associated cavity Reynolds number. Adjacent cavities can also influence the wake flow behavior. For instance, the cavity position has an influence on the turbulent flow region even after the natural laminar-turbulent flow transition line, and many cavities close to each other shift the transition line towards the leading edge. With increasing aspect ratio as well as increasing inflow velocity, the wake flow turbulence and, thus, the visibility of the wake in the thermographic images becomes higher. However, even if the wake flow from the cavity to the transition line is not continuously visible, a change in the natural transition can still be observed. As a result, thermographic flow visualization enables the indirect detection of cavity-type defects on airfoils and can provide further information about the cavity features.

Wind turbines have grown in size in recent years, making efficient structural health monitoring of all of their structures even more important. Wind turbine blades deform elastically under the loads applied to them by wind and inertial forces acting on the rotating rotor blades. In order to properly analyze these deformations, an earthbound system is desirable that can measure the blade deformation, as well as the tower–blade tip clearance from a large measurement working distance of over 150 m and a single location. To achieve this, a terrestrial laser scanner (TLS) in line-scanning mode with vertical alignment is used to measure the distance to passing blades and the tower for different wind loads over time. In detail, the blade deformations for two different wind load categories are evaluated and compared. Additionally, the tower–blade tip clearance is calculated and analyzed with regard to the rotor speed. Using a Monte Carlo simulation, the measurement uncertainty is determined to be in the millimeter range for both the blade deformation analysis and the tower–blade tip clearance. The in-process applicable measurement methods are applied and validated on a 3.4 MW wind turbine with a hub height of 128 m. The deformation of the blade increases with higher wind speed in the wind direction, while the tower–blade tip clearance decreases with higher wind speed. Both relations are measured not only qualitatively but also quantitatively. Furthermore, no difference between the three rotor blades is observed, and each of the three blades is shown to be separately measurable. The tower–blade tip clearance is compared to a reference video measurement, which recorded the tower–blade tip clearance from the side, validating the novel measurement approach. Therefore, the proposed setup and methods are proven to be effective tools for the in-process structural health monitoring of wind turbine blades.

Abstract Thermographic flow visualization is already an established imaging method to localize the laminar-turbulent flow transition on the rotor blades of operating wind turbines, while a steady flow state is assumed. To understand the potential of thermographic flow visualization for the investigation of unsteady flow phenomena, its capability to detect the change of the flow transition position due to a wind gust is studied. Previously laminar flow regions become turbulent with the gust, which means a sudden increase of heat transfer between surface and fluid and, thus, a decrease of surface temperature. The latter is detected by evaluating the difference of thermographic images before and during the wind gust. The achievable sensitivity and the temporal resolution are limited by the thermodynamic properties of the rotor blade and the fluid flow, as well as by the natural rotor blade heating with the sun’s radiation. As a result of theory and experiments on real wind turbines, the feasibility to detect flow state changes in the order of seconds is proven. This opens upthe analysis of unsteady flow phenomena on wind turbines by means of thermographic flow visualization.

Defects such as voids, which may occur during the manufacturing of wind turbine blades, have a significant impact on premature rain erosion, especially at the leading edge. Active thermography offers the potential for non-contact in-situ inspection of rotor blade leading edges. Thermographic investigations on curved and coated GFRP specimens have shown that sub-surface defects not only represent a susceptibility to erosion, but that the onset of erosion is dependent on the depth of the defects. Furthermore, the damage development of sub-surface towards surface defects can be visualized, which allows the damage stage to be assigned to an erosion stage.

Premature rain erosion damage development at the leading edges of wind turbine rotor blades impair the efficiency of the turbines and should be detected as early as possible. To investigate the causes of premature erosion damage and the erosion evolution, test specimens similar to the leading edge of a rotor blade were modified with different initial defects, such as voids in the coating system, and impacted with waterdrops in a rain erosion test facility. Using CT and XRM with AI-based evaluation as non-destructive measurement methods showed that premature erosion arises from the initial material defects because they represent a weak point in the material composite. In addition, thermographic investigations were carried out. As it shows results similar to the two lab-based methods, active thermography has a promising potential for future in-situ monitoring of rotor blade leading edges.

Defects or contaminations on a wind turbine’s rotor blade can lead to wedge-shaped regions with turbulent flow, which can decrease the energy output of the wind turbine. An automated algorithm is proposed, which detects turbulence wedges in thermograms even for low contrast-to-noise ratios by using prior knowledge about the wedge shape. The algorithm is verified with simulated thermograms and validated with a measured thermogram of a rotor blade of an in-service wind turbine.Even at a contrast-to-noise ratio of 2, all turbulence wedges are detected and their dimensions are quantified. Additionally,the results are mapped to the blade’s geometry to realize 3D thermography

An IR thermography-based detection and localization of turbulent flow separation at an operating wind turbine is presented and verified for the first time. Turbulent flow separation limits the efficiency of wind turbines and causes increased structural loads and acoustic emissions. IR thermography is an established measurement method for stall detection in wind tunnel experiments, however a transfer to operating wind turbines is an open research question. With respect to the state of the art for thermographic stall detection, a novel thermographic measurement approach for a feature-based stall detection is presented, verified and applied on wind turbines. The measurement approach evaluates the surface temperature response to unsteady inflow conditions and enables an unambiguous detection of flow separation by means of temperature fluctuation maxima in the regions of flow transition as well as an increasing temperature fluctuation within the separated flow region. Finally, the aim to obtain a non-invasive and in-process detection of flow separation on an operating wind turbine with IR thermography is achieved and verified using tufts flow visualization.

A thermographic detection and localization of unsteady flow separation on an operating wind turbine of type GE1.5sl is presented and verified by means of tufts flow visualisation. Unsteady flow separation phenomena such as dynamic stall are an undesired flow state as it causes fatigue failures, limits the turbine efficiency and increases noise emissions from the rotor blades. In comparison to available methods for stall detection on wind turbines, the presented infrared thermographic measurement approach is non-invasive, in-process capable and provides a high spatial resolution. On the basis of the thermodynamic response behaviour of the surface temperature in case of unsteady flow events, a two-step signal processing approach is proposed, to achieve the highest possible spatio-temporal resolution in the detection and localisation of stall. First, the identification of distinct maxima of the spatial standard deviation of difference images, enables to determine potential stall events in time. In the subsequent combined image evaluation with a transient approach and a principal component analysis, unsteady flow separation is detected during the occurrence of a strong wind gust with the maximum time resolution (image exposure time) as well as the maximum spatial resolution (image resolution), respectively, despite the limited signal-to-noise ratio compared to wind tunnel experiments. In addition, a geometric assignment of the image data to the rotor blade geometry is conducted, which enables a localization of the separation point on the 3 days rotor blade geometry with a maximal uncertainty of 2.3% of the chord length.

For the first time, a thermographic detection and localization of turbulent flow separation on an operating wind turbine is presented and verified. Flow separation on wind turbine rotor blades causes power reduction, structural loads and increased noise emissions. In contrast to established methods for stall detection, the presented infrared thermographic measurement approach is non-invasive, in-process capable and provides a high spatial resolution. With respect to the state of the art for thermographic stall detection in wind tunnel experiments, the thermal surface response to unsteady inflow conditions is evaluated for measurements on an operating wind turbine, in order to achieve unambiguous thermographic features for the detection of flow separation. The evaluation of the thermodynamic response behavior shows a clear detection of flow separation by means of temperature fluctuation maxima in the regions of flow transition as well as an increasing temperature fluctuation within the separated flow region. In addition, a geometric assignment is conducted which enables a localization of the separation point with an uncertainty of 0.6% of the chord length. The detection and localization of flow separation is verified by means of tufts visualization.

Thermographic flow visualization is a non-contact, non-invasive approach for assessing the aerodynamic state of wind turbine rotor blades and, as a result, the overall efficiency of the wind turbine. The distinguishability between the laminar and turbulent flow regimes in operating wind turbines cannot be easily increased intentionally and is totally dependent on the energy input from the sun. To deal with low-contrast measurement conditions and improve the distinguishability between flow regimes, advanced image processing using the feature extraction method principal component analysis is used. The image processing is applied to an image series of thermographic flow visualizations of a steady flow situation in a free-field experiment on a wind turbine in operation with a low distinguishability between the laminar and turbulent flow regime. The resulting feature images, based on the temporal intensity fluctuations in the images, are evaluated with regard to the global distinguishability between the laminar and turbulent flow regime. By applying the principal component analysis, the contrast-to-noise ratio was increased by a factor of 2.5. Furthermore, the resulting flow visualizations enable a localization of the laminar-turbulent flow transition, that is not possible in the raw data due to missing features in the intensity profile that allow a clear separation.

Abstract The transformation of the energy system towards a sustainable reduction of CO2 emissions and the consequently rising contribution of fluctuating energy sources to the German energy system leads to new challenges. In particular, the sufficiency of the present powerline system is of interest. To evaluate the German network sufficiency, a data‐based modelling is used to measure the number of intervals with insufficient transport capabilities as well as the yearly sum of infeasible power transports. In order to do so, the actual residual loads are calculated from the power data in the database “GEOWISOL” and the data on the German power grid are obtained from the project “SciGrid.” The temporal resolution of power data is 15 min, and the spatial resolution refers to the 2‐digit ZIP code regions. The present transmission network is shown to be insufficient in a linearly increasing number of intervals for scenarios with increasing renewable power production. The uncertainties of the results presented are also investigated by means of a Monte Carlo simulation with the known uncertainties of the power data. The simulation results in a relative uncertainty for the number of intervals with insufficient transport capabilities of the network of roughly 1%. Thus, it can be used in the future either to investigate network expansion plans or to validate network expansion studies with simulated data or synthetic networks.

The fluctuations of the renewable energies request storage systems as a significant part of the future energy system. Here we introduce a measurement approach regarding the regional coverage of the power demand and the influence of a storage system on it. The approach is based on measured power data with given uncertainties. The resolution is 15 minutes in the time domain and 2-digit ZIP code regions in the spatial domain. An idealized storage model is introduced, and one storage is implemented for each region. As a result, the regional coverage is measured for different storage parameters and the respective measurement uncertainty is assessed to be below the set goal of 2 %. Furthermore, the inverse problem, i. e. the storage dimensioning, is investigated by estimating the necessary storage parameters to achieve a desired regional coverage. Findings: electrical self-sufficiency can be reached in 23 out of 95 regions, in contrast in 56 regions the regional coverage is increased insignificantly regardless of the parameters of the storage system. This illustrates the necessity of power transport to enable the increase of regional coverage. Concluding, the proposed measurement approach enables the estimation of storage parameters and can be used for storage dimensioning.

Defects on rotor blade leading edges of wind turbines can lead to premature laminar–turbulent transitions, whereby the turbulent boundary layer flow forms turbulence wedges. The increased area of turbulent flow around the blade is of interest here, as it can have a negative effect on the energy production of the wind turbine. Infrared thermography is an established method to visualize the transition from laminar to turbulent flow, but the contrast-to-noise ratio (CNR) of the turbulence wedges is often too low to allow a reliable wedge detection with the existing image processing techniques. To facilitate a reliable detection, a model-based algorithm is presented that uses prior knowledge about the wedge-like shape of the premature flow transition. A verification of the algorithm with simulated thermograms and a validation with measured thermograms of a rotor blade from an operating wind turbine are performed. As a result, the proposed algorithm is able to detect turbulence wedges and to determine their area down to a CNR of 2. For turbulence wedges in a recorded thermogram on a wind turbine with CNR as low as 0.2, at least 80% of the area of the turbulence wedges is detected. Thus, the model-based algorithm is proven to be a powerful tool for the detection of turbulence wedges in thermograms of rotor blades of in-service wind turbines and for determining the resulting areas of the additional turbulent flow regions with a low measurement error.

Thermographic flow visualization is a contactless, non-invasive technique to visualize the boundary layer flow on wind turbine rotor blades, to assess the aerodynamic condition and consequently the efficiency of the entire wind turbine. In applications on wind turbines in operation, the distinguishability between the laminar and turbulent flow regime cannot be easily increased artificially and solely depends on the energy input from the sun. State-of-the-art image processing methods are able to increase the contrast slightly but are not able to reduce systematic gradients in the image or need excessive a priori knowledge. In order to cope with a low-contrast measurement condition and to increase the distinguishability between the flow regimes, an enhanced image processing by means of the feature extraction method, principal component analysis, is introduced. The image processing is applied to an image series of thermographic flow visualizations of a steady flow situation in a wind tunnel experiment on a cylinder and DU96W180 airfoil measurement object without artificially increasing the thermal contrast between the flow regimes. The resulting feature images, based on the temporal temperature fluctuations in the images, are evaluated with regard to the global distinguishability between the laminar and turbulent flow regime as well as the achievable measurement error of an automatic localization of the local flow transition between the flow regimes. By applying the principal component analysis, systematic temperature gradients within the flow regimes as well as image artefacts such as reflections are reduced, leading to an increased contrast-to-noise ratio by a factor of 7.5. Additionally, the gradient between the laminar and turbulent flow regime is increased, leading to a minimal measurement error of the laminar-turbulent transition localization. The systematic error was reduced by 4% and the random error by 5.3% of the chord length. As a result, the principal component analysis is proven to be a valuable complementary tool to the classical image processing method in flow visualizations. After noise-reducing methods such as the temporal averaging and subsequent assessment of the spatial expansion of the boundary layer flow surface, the PCA is able to increase the laminar-turbulent flow regime distinguishability and reduce the systematic and random error of the flow transition localization in applications where no artificial increase in the contrast is possible. The enhancement of contrast increases the independence from the amount of solar energy input required for a flow evaluation, and the reduced errors of the flow transition localization enables a more precise assessment of the aerodynamic condition of the rotor blade.

The contribution of fluctuating wind and solar energy sources to the German electrical energy demand has increased from 12% up to 40% within the last 10 years and leads to higher infrastructure requirements due to the different spatiotemporal characteristics of wind and solar energy generation. However, common investigations of the energy system exhibit a limited spatiotemporal resolution of power or are based only on simulation models. Therefore, real measurement data regarding the energy demand and the generated energy from 95 German ZIP code regions are investigated to support current infrastructure decisions for the power system. In particular, the residual regional power generation with different wind and solar shares and the resulting power flows are calculated as the solution of a cost optimization problem without energy storage. As a result, a heterogeneous power distribution is identified with a medium surplus in northern and eastern regions and deficits in western and southern Germany. Furthermore, maximum regional coverage of the energy demand by solar and wind energy exists with respect to the share of wind and solar energy generation. For an extrapolation scenario of the average renewable energy to 100% of the average demand, the optimal share is 53% solar and 47% wind power. As a conclusion, a balance of the spatiotemporal fluctuations between renewable energy generation and demand is feasible with appropriate changes in the power grid system, e.g., load management.

Model-inspired signal processing approaches with an enhanced detectability of flow separation on thermographic images are presented. Flow separation causes performance loss, structural loads and increasing acoustic emissions on wind turbine rotor blades. However, due to the low thermal contrast between turbulent and separated flow regions, the non-invasive thermographic visualisation of flow separation is currently only possible for wind tunnel measurements, which are characterised by a high thermal contrast and a small measuring distance. The state-of-the-art signal processing approaches evaluate the surface temperature fluctuation of thermographic image series. However, understanding of the signal measurement chain with a distinct consideration of the influences on the dynamic surface temperature is incomplete. Therefore, designing model-inspired signal processing approaches which provide a high interpretability and a maximum contrast is an open task. The proposed signal processing approaches evaluate the surface response selectively, by using the amplitude information of the surface temperature response to an oscillating input signal or gradient-based for a transient input signal. The approaches are applied to wind tunnel measurements on a rotor blade profile at a near thermodynamic steady state and a transient thermodynamic behaviour at Reynolds numbers that are representative for operational wind turbines. The gradient-based evaluation shows an improved contrast for the detection of flow separation, but is only applicable to profiles with transient thermodynamic behaviour. The amplitude evaluation provides a high degree of interpretability of the processed images based on flow-dependent features and enables for an unambiguous identification of flow separation by a global amplitude minimum close to the separation point. Additionally, an increased spatial resolution for surface modifications is shown, while the contrast between flow regions is significantly decreased. Hence, the proposed approaches allow for an improved identifiability of flow separation with regard to future applications on wind turbines in operation.

Initial defects, for example, those occurring during the production of a rotor blade, encourage early damages such as rain erosion at the leading edge of wind turbine rotor blades. To investigate the potential that initial defects have for early damage, long-pulse thermography as a non-destructive and contactless measurement technique is applied to a strongly curved and coated test specimen for the first time. This specimen is similar in structural size and design to a rotor blade leading edge and introduced with sub-surface defects whose diameters range between 2mm and 3.5mm at depths between 1.5mm and 2.5mm below the surface. On the curved and coated test specimen, sub-surface defects with a depth-to-diameter ratio of up to 1.04 are successfully detected. In particular, defects are also detectable when being observed from a non-perpendicular viewing angle, where the intensity of the defects decreases with increasing viewing angle due to the strong surface curvature. In conclusion, long-pulse thermography is suitable for the detection of sub-surface defects on coated and curved components and is therefore a promising technique for the on-site application during inspection of rotor blade leading edges.

Wind turbine plants have grown in size in recent years, making an efficient structural health monitoring of all of their structures ever more important. Wind turbine towers deform elastically under the loads applied to them by wind and inertial forces acting on the rotating rotor blades. In order to properly analyze these deformations, an earthbound system is desirable that can measure the tower’s movement in two directions from a large measurement working distance of over 150 m and a single location. To achieve this, a terrestrial laser scanner (TLS) in line-scanning mode with horizontal alignment was applied to measure the tower cross-section and to determine its axial (in the line-of-sight) and lateral (transverse to the line-of-sight) position with the help of a least-squares fit. As a result, the proposed measurement approach allowed for analyzing the tower’s deformation. The method was validated on a 3.4 MW wind turbine with a hub height of 128 m by comparing the measurement results to a reference video measurement, which recorded the nacelle movement from below and determined the nacelle movement with the help of point-tracking software. The measurements were compared in the time and frequency domain for different operating conditions, such as low/strong wind and start-up/braking of the turbine. There was a high correlation between the signals from the laser-based and the reference measurement in the time domain, and the same peak of the dominant tower oscillation was determined in the frequency domain. The proposed method was therefore an effective tool for the in-process structural health monitoring of tall wind turbine towers.

Background and objective: Glaucoma is currently a major cause for irreversible blindness worldwide. A risk factor and the only therapeutic control parameter is the intraocular pressure (IOP). The IOP is determined with tonometers, whose measurements are inevitably influenced by the geometry of the eye. Even though the corneal mechanics have been investigated to improve accuracy of Goldmann and air pulse tonometry, influences of geometric properties of the eye on an acoustic self-tonometer approach are still unresolved. Methods: In order to understand and compensate for measurement deviations resulting from the geometric uniqueness of eyes, a finite element eye model is designed that considers all relevant eye components and is adjustable to all physiological shapes of the human eye. Results: The general IOP-dependent behavior of the eye model is validated by laboratory measurements on porcine eyes. The difference between simulation and measurement is below 8 mu m for IOP levels from 5 to 40 mmHg. The adaptive eye model is then used to quantify systematic uncertainty contributions of a variation of eye length and central corneal thickness based on input statistics of a clinical trial series. The adaptive eye model provides the required relation between biometric eye parameters and the corneal deflection amplitude, which here is the measured quantity to trace back to the IOP. Implementing the relations provided by the eye model in a Gaussian uncertainty propagation calculation now allows the quantification of the uncertainty contributions of the biometric parameters on the overall measurement uncertainty of the acoustic self-tonometer. As a result, a systematic uncertainty contribution resulting from deviations in eye length dominate stochastic deviations of the sensor equipment by a factor of 3.5. Conclusion: As perspective, the proposed adaptive eye model provides the basis to compensate for systematic deviations of (but not only) the acoustic self-tonometer. (c) 2021 Elsevier B.V. All rights reserved.

Environmental conditions like the presence of rainfall or insects can disturb the rotor blade surface of wind turbines in operation, triggering a premature laminar-turbulent flow transition in the boundary layer flow. The local contaminations develop a wedge-shaped surface area of turbulent flow in the area that would otherwise be laminar if the surface would be undisturbed, decreasing the size of the laminar flow regime. This change in the ratio between overall laminar and turbulent flow regime sizes has a negative impact on the aerodynamic performance of the profile, decreasing the efficiency of the wind turbine. While the spatial distribution of the flow regimes can be visualized with thermographic flow visualization, the state-of-the-art image processing method for applications on wind turbines in operation is not robust against localizing the position of the flow transition along these turbulence wedges. Therefore, this work introduces an advancement of the image processing method for localizing the flow transition in thermographic images with a focus on decreasing the localization uncertainty along the turbulence wedges. The state-of-the-art one-dimensional evaluation method is enhanced by a two-dimensional image processing method in order to increase the directional gradients at the turbulence wedges' flanks. Six out of six previously undetected turbulence wedges are successfully detected in a flow visualization image of a rotor blade of a GE 1.5 sl wind turbine in operation. The new approach yields an improved application of the thermographic flow visualization for locating the flow transition and quantifying the reduction of the laminar flow area on disturbed rotor blade surfaces of wind turbines in operation.