Intelligent blade for wind turbines

Maintenance and repair of wind turbine structures have become more challenging and at the same time essential as they evolve into larger dimensions or located in places with limited access. Even small structural damages may invoke catastrophic detriment to the integrity of the system. So, cost-effective, predictive, and reliable structural health monitoring (SHM) system has been always desirable for wind turbines. A real-time nondestructive SHM technique based on multisensor data fusion is proposed in this paper. The objective is to critically analyze and evaluate the feasibility of the proposed technique to identify and localize damages in wind turbine blades. The structural properties of the turbine blade before and after damage are investigated through different sets of finite-element method simulations. Based on the obtained results, it is shown that information from smart sensors, measuring strains, and vibrations data, distributed over the turbine blades can be used to assist in more accurate damage detection and overall understanding of the health condition of blades. Data fusion technique is proposed to combine these two diagnostic tools to improve the detection system that provides a more robust reading with reduced false alarms.

Wind turbine blades are prone to icing under extreme working conditions, which will reduce the efficiency of wind power generation and seriously affect the service life of wind power system. Therefore, the anti-icing problem of wind turbine blades has become one of the hot topics in wind power technology research. It is of great practical significance to develop anti-icing/deicing technology for wind turbine blades for the safe and effective operation of wind power. In this paper, the numerical simulation optimization design for composite material wind turbine blades anti-/deicing component was studied, and the orthogonal optimization method was employed to optimize the parameters of the wind turbine blade to prevent ice formation. The optimization group parameter group is obtained. Through the wind turbine blade temperature distribution simulation, the correctness of the optimization results of the composite material anti-/deicing component was analyzed. The purpose of this paper is to optimize the heat transfer of anti-/deicing component for composite material wind turbine blade, and lay a theoretical and technical foundation for effectively solving the problem of wind turbine blade icing, which proposing a new composite wind turbine blade anti-/deicing scheme.

A review of full-scale structural testing of wind turbine blades

Ultrasonic testing (UT) is an effective method for detecting internal damage in wind turbine blades. However, manual interpretation is time-consuming and subjective. To automate UT in the wind turbine industry, an image detection model based on deep learning called UCD-YOLO (Ultrasonic C-scan image Detection You Only Look Once) is proposed to detect internal damage such as stratified and lack of glue in wind turbine blades. In order to enhance the feature extraction ability under complex damage forms, the use of deformable convolutional networks and context augmentation module are proposed. Designing a lightweight cross stage partial spatial pyramid pool fast (LCSP_SPPF) module, which allows the model to learn more information and improve detection accuracy. The introduction of wise intersection over union (WIoU) improves the convergence speed while reducing the regression error. Using UT devices to collect damage images of wind turbine blades and conducting comprehensive evaluation experiments on the detection performance of the proposed method. Results show that compared to the YOLOv5, UCD-YOLO is not only lighter but also achieves a 7.5% increase in mean average precision (mAP50). And compared with other detection methods, further verifying that the proposed method can quickly and effectively detect wind turbine blade internal damage, improving power generation efficiency.

Structure damage modelling for the simulation of elastic wave propagation is essential for simulation-based testing of structural health monitoring (SHM) systems. Reflection, transmission and other complex phenomena related to elastic wave interaction with structure defects need to be reproduced by the simulation method to replace physical testing by numerical one and aid the design process of SHM system. Authors review commonly used methods for the simulation of elastic wave propagation pointing out their advantages and disadvantages. Subsequently methods for crack and delamination modelling are presented. Proposed models are used for numerical testing of designed SHM system. Simulation results are compared with experimental ones, showing very good agreement. Finally, structure damage models are used for testing delamination localization algorithm.

The detection of structural damage in wind turbine blades is essential for ensuring operational reliability and preventing unexpected failures. This work presents a method based on the Markov Transition Field (MTF) and Convolutional Neural Networks (CNNs) for automatic classification of blade conditions using vibration signals. MTF transforms time-series data into two-dimensional representations that preserve temporal transition patterns, enabling CNNs to extract spatial features associated with structural degradation. The vibration signals were acquired from the Y-axis, which has shown greater sensitivity to damage-related information in rotating systems. Four severity levels were considered: healthy, light, intermediate, and severe. Analysis was conducted at 7 revolutions per second (rps), selected as an intermediate point between the start-up (3 rps) and maximum operational speed (12 rps), allowing for the capture of representative dynamics under partial load conditions. The proposed MTF-CNN model achieved 100% classification accuracy, demonstrating its effectiveness for identifying damage severity in wind turbine blades and its potential for integration into condition monitoring systems.

Application of videometric technique to deformation measurement for large-scale composite wind turbine blade

Structural Health Monitoring (SHM) systems are widely regarded as capable of significantly reducing inspection costs of safety-critical structures in industries such as aerospace, nuclear, and oil and gas, among others. Successful SHM systems can be considered those which combine good sensitivity to defects, preferably with the capability of localization and identification, with a low sensor density. Techniques based on sparse arrays of sensors which generate and receive guided waves are among the most promising candidates. Guided waves propagate over large distances and certain modes have the ability to transmit through a variety of structural features leading to a relatively small number of distributed sensors being able to cover the structure. In complex structures, which contain high densities of structural elements, the timetraces obtained are often too complex to be directly interpreted due to the large number of overlapping reflections. In this case, the Baseline Subtraction technique becomes attractive. In this method a current signal from the structure is subtracted from a signal which has been acquired during the initial stages of operation of the structure. This eliminates the need for interpretation of the complex raw time signal and any defects will be clearly seen provided the amplitude of the residual signal obtained after subtraction of the baseline signal is sufficiently low when the structure is undamaged. However, it is well known that environmental effects such as stress, ambient temperature variations and liquid loading affect the velocity of guided waves; this modifies the time-traces and leads to high levels of residual signal if a single baseline, taken under different conditions, is used. Of these effects, temperature variations are the most commonly encountered and are critical since they affect not only the wave propagation but also the response of transducers. The present work aims to demonstrate the potential of guided wave health monitoring of large area complex structures. It starts with a general literature review on inspection and monitoring of large area structures, in which the advantages and disadvantages of this technique compared to other well-established SHM techniques 2 are presented. The design and behaviour of two different temperature-stable transducers generating high A0 or S0 mode purity in the sub-200kHz frequency region are described. The efficiency of different signal processing techniques aimed at reducing or eliminating the influence of temperature on wave propagation is evaluated and a temperature compensation signal processing strategy is proposed. Finally, a large metallic structure is used to demonstrate a sparse-array SHM system based on this signal processing strategy, and imaging algorithms are used to combine the information from a large number of sensor combinations, ultimately leading to the localization of defects artificially introduced in the structure.

An effect assessment and prediction method of ultrasonic de-icing for composite wind turbine blades

As wind power generation technology advances rapidly, the maintenance and management of wind turbine blades have gained significant importance. Damage to these blades can lead to severe repercussions, including threats to personnel safety, economic losses, and in extreme cases, fuel spills and environmental contamination. To achieve prompt and precise automatic monitoring of wind turbine blade damage, this paper introduces a damage identification method for wind turbine blades using an enhanced YOLOv8 algorithm. In comparison to other YOLO series algorithms, this method enhances recognition accuracy while reducing model size, accelerating computational speed, and shortening detection time. By utilizing this algorithm, we can improve the efficiency and accuracy of wind turbine blade damage detection, thereby lowering maintenance costs, mitigating safety, economic, and environmental risks, and enhancing the stability of wind farm operations.

Computational modelling of repetitive raindrop impact and predicting the ensuing damage in the coating and substrate due to fatigue is an essential step in determining the life of the wind turbine blade. The blade is multi-layered with a viscoelastic coating layer, a viscoelastic putty layer, and an elastic composite substrate with an interface between the layers. The damage usually takes the form of erosion of the coating and debonding at the various interfaces between the layers of the wind turbine blade. The modelling of impact and damage involves several steps beginning with generating a stochastic rain scenario which provides information on raindrop size distribution and velocity hitting a volume of the blade. A Coupled Eulerian-Lagrangian (CEL) analysis of a single raindrop and a representative region of the blade provides impact forces on and resulting stresses in the blade. The CEL analysis is repeated for drops of different diameters to generate a library of stresses. This library, combined with a stochastic rain scenario, generates stresses at any location of the representative blade due to the repetitive impact of different-sized raindrops hitting arbitrary locations. Continuum damage mechanics is used to predict the damage evolution in the coating, and when damage reaches a critical value in any element within the developed finite element model of the multilayered blade sample represents the erosion of the coating. A cohesive zone model for fatigue debonding is employed for interfaces between coating, putty, and composite substrate. Both the Continuum damage mechanics model and a Cohesive zone model for fatigue are implemented through a user-defined subroutine in ABAQUS/Explicit. Using the model, the interaction between erosion and debonding is studied. Using the properties of materials from the literature, the analysis shows that damage always initiates from the bottom portion of the coating layer close to the adhesive interface. This eventually creates displacement jumps at the interface of coating and putty, causing damage initiation at the interfaces. Repetitive raindrop impact of different sizes at arbitrary locations causes damage accumulation and propagation faster at the interface, finally damaging the coating layer.

Interdigital transducers (IDT) generate and receive ultrasonic surface waves without the complexity involved with secondary devices such as angled wedges or combs. The IDT sensors have been successfully applied for the NDE of homogeneous materials like metals in order to detect cracks and de‐bond. However, these transducers have not been yet adapted for complex and anisotropic materials like fiber‐reinforced composites. This work presents the possibility of using IDT sensors for monitoring structural damages in wind turbine blades, typically made of fiberglass composites. IDT sensors with a range of operating frequency between 250 kHz and 1 MHz are initially tested on representative composite test panels for ultrasonic surface wave properties including beam spread, propagation distance and effect of material’s anisotropy. Based on these results, an optimum frequency range for the IDT sensor is found to be 250–500 kHz. Subsequently, IDT sensors with operating frequency 500 kHz are used to detect and quantify artificial defects created in the composite test samples. Discussions are made on the interaction of ultrasonic fields with these defects along with the effects of fiber directionality and composite layer stacking.

Erosion of the leading edge is one of the most severe forms of damage in wind turbine blades, particularly in offshore wind farms. This degradation, mainly caused by rain, sand, and airborne particles through droplet impingement wear, significantly decreases blade aerodynamic efficiency and power output. Since blades, typically made of fiber-reinforced polymer composites, are the most expensive components of a turbine, developing protective coatings is essential. In this study, polyurethane (PU) composite coatings reinforced with titanium dioxide (TiO2) particles were added on glass fiber substrates by spray coating. The incorporation of TiO2 improved the mechanical and electrochemical performance of the PU coatings. FTIR and XRD confirmed that low TiO2 loadings (1 and 3 wt%) were well dispersed within the PU matrix due to hydrogen bonding between TiO2 –OH groups and PU –NH groups. The PU/TiO2 3% coating exhibited ~61% lower corrosion current density (I_corr) compared to neat PU, indicating superior corrosion resistance. Furthermore, uniform TiO2 dispersion resulted in statistically significant improvements (p < 0.05) in hardness, yield strength, elastic modulus, and adhesion strength. Overall, the PU/TiO2 coatings, particularly at 3 wt% loading, show strong potential as protective materials for wind turbine blades, given their enhanced mechanical integrity and corrosion resistance.

Modeling concept and numerical simulation of ultrasonic wave propagation in a moving fluid-structure domain based on a monolithic approach

Composite wind turbine blades may experience interlaminar damage, including adhesive failure, cracking, and interlaminar fracture failure, from manufacturing or external fatigue load. Among these, the adhesive failure of adhesive joints is critical. Therefore, it is important to identify the failure mechanism in the adhesive joints between the spar cap–shear web and trailing edge of composite blades. We calculated fracture toughness through Mode I, Mode II, and Mixed-mode tests for quantitative analysis of adhesive joints. Then, to select a modeling method for realizing the damage generated in the blade, the method was verified from the specimen level. A damage model was constructed, considering contact conditions of the spar cap–shear web and trailing edge for an NREL 5MW wind turbine blade. Finally, a damage model based on cohesive zone modeling was used to analyze the progressive failure behavior of debonding at adhesive joints according to the external force applied to the blade.