Monitoring Of Wind Turbine Blades Research Articles

The Design and Ground Test Verification of an Energy-Efficient Wireless System for the Fatigue Monitoring of Wind Turbine Blades Based on Bistable Piezoelectric Energy Harvesting.

A wireless monitoring system based on piezoelectric energy harvesting (PEH) is presented to provide fatigue data of wind turbine blades in operation. The system comprises three subsystems, each respectively providing the following functions: (i) the conversion of mechanical to electric energy by exploiting the bistable vibration of a composite beam with piezoelectric patches in post-buckling, (ii) harvesting the converted energy by means of a modified, commercial, off-the-shelf (COTS) circuit to feed a LiPo battery and (iii) the battery-powered acquisition and wireless transmission of sensory signals to the cloud to be elaborated upon by the end-user. The system was verified with ground tests under representative operation conditions, which demonstrated the fulfillment of the design requirements. The measurements indicated that the system provided 23% of the required power for fully autonomous operation when subjected to white noise base excitation of 1 g acceleration in the range of 1-20 Hz.

Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations

AbstractThe present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.

A full-scale wind turbine blade monitoring campaign: detection of damage initiation and progression using medium-frequency active vibrations

This work is concerned with a structural health monitoring campaign of a 52-m wind turbine blade. Multiple artificial damages are introduced in the blade sequentially, and fatigue testing is conducted with each damage in sequence. Progressive fatigue-driven damage propagation is achieved, enabling investigations concerning detection of initiation and propagation of damage in the blade. Using distributed accelerometers, operational modal analysis is performed to extract the lower-order natural vibration modes of the blade, which are shown to not be sensitive to small damages in the blade. To enable monitoring of small damages, an active vibration monitoring system is used, comprised of an electrodynamic vibration shaker and distributed accelerometers. From the accelerometer data, frequency domain methods are used to extract features. Using the extracted features, outlier detection is performed to investigate changes in the measurements resulting from the introduced damages. Capabilities of using features based on the active vibration data for detection of initiation and progression of damage in a wind turbine blade during fatigue testing are investigated, showing good correlation between the observed damage progression and the calculated changes in the damage index.

Assessing the rotor blade deformation and tower–blade tip clearance of a 3.4 MW wind turbine with terrestrial laser scanning

Abstract. 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.

Snap-through energy harvester with buckled mechanism and hierarchical auxetic structures for ultra-low-frequency rotational excitations

In this Letter, a snap-through energy harvester is proposed to break through the energy output bottleneck of ultra-low-frequency (<1 Hz) rotational energy harvesting. On one hand, a buckled mechanism provides large-amplitude snap-through motion that enhances the output power. On the other hand, the hierarchical auxetic structures enable the simultaneous operation of d31 and d32 modes of piezoelectric buzzers and boosts the energy harvested. Moreover, both the buckled mechanism and auxetic structures can reduce the fundamental natural frequency of the total system. A finite element model is established to predict the harvester performances, which are validated via experiments. Experimental results show that the integration of the buckled mechanism and auxetic structures can improve the output power by 3224.75% at 0.5 Hz. Specifically, the proposed harvester can achieve an output power of 146.2 μW and a normalized power density of 1.392 μW/mm3 Hz2 at 0.5 Hz, which are superior to other state-of-the-art rotational piezoelectric energy harvesters. Therefore, the proposed harvester can provide sufficient energy for low-power sensors at ultra-low rotational frequencies and has a great application potential in the structural health monitoring of wind turbine blades.

Research on infrared nondestructive testing and thermal effect analysis of small wind turbine blades under natural excitation

In recent years, with the increase of wind power generation and the rapid growth of the number of wind turbines installed, the safety monitoring of wind turbine blades has also attracted wide attention. To further study the thermal effect of wind turbine damaged blades under natural excitation in summer in northern China, based on the outdoor infrared non-destructive testing of wind turbine blades, a numerical simulation of wind turbine blades with wear damage is carried out by using the fluid solid heat transfer theory in COMSOL multi-physical field coupling software. The best detection light condition is obtained, in which the detection effect of wear damage is the best when the light intensity reaches 1000 W.m−2 or above at noon. The simulation results are basically in agreement with the experimental results. And compared with the previous physical modeling method using natural convection heat transfer coefficient to replace wind speed, the physical modeling method considering wind speed and air humidity is more consistent with the experimental results, the maximum error value was decreased by 7.46 %, which verifies the rationality of the model and the feasibility of the method. In addition, the blade works in the natural environment for a long time, the thermal stress will have a significant impact on its fatigue strength, so on the basis of this model, the coupling analysis, and then analyze the change of thermal stress in different wind speed. With the increase of wind speed, the temperature on the blade surface gradually decreases and the wind pressure gradually increases. Because the influence of temperature on the stress is greater than that of wind pressure, the stress also shows a gradual downward trend.

Computational investigation into the effect of material properties on aeroacoustics based damage detection from wind turbine blades

A wind turbine blade monitoring system would provide significant benefit to wind farm owners and operators by reducing operation and maintenance costs and increasing energy production by limiting turbine downtime. Previous work has demonstrated potential success of a system based on monitoring the flow-generated sound with blade internal cavity microphones. In this study, the effect of blade material acoustic properties is investigated to provide recommendations on the potential application of this approach. A reduced-order model is used to identify the flow-generated lumped-parameter acoustic sources outside the blade at a frequency of 10 kHz. A feasible range of acoustic properties is used for the blade and shear web acoustic transmission coefficient and acoustic reflection coefficient. Damage detection success improved as reflection increased, as more sound propagated toward to the root of the blade. However, the damage detection success rate did not consistently increase with an increase or decrease in the transmission coefficient and balance must be achieved depending on the specific blade and the expected flow-generated sound. These results demonstrate the usefulness of quantifying the blade material acoustic properties and will be used to strategically design a blade monitoring system.

Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy

To improve the efficiency and reliability of wind power generation, condition monitoring of wind turbines has drawn extensive attention worldwide. However, blade health monitoring is still challenging because of volatile operating conditions and the dependence on the assumption that healthy and unhealthy measurements can be naturally separated after the training stage. In this paper, a self-supervised health representation learning method is proposed to address these problems, and only healthy measurements are required in training. Specifically, data representations related to blade health conditions are learned by neural networks though data augmentation and an auxiliary task. In this case, the interference of operating circumstances and noise can be eliminated, and the volatility of measurements can be suppressed to establish accurate models of healthy operations. Moreover, the separability assumption is guaranteed by imposing constraints on the representation distributions of unhealthy samples, improving the reliability of decision making based on the learned knowledge. Blade health conditions are recognized using kernel density estimation. The satisfactory performance of the proposed method is demonstrated through laboratory and field measurements, achieving higher accuracy than existing approaches for online health monitoring. This work contributes to the economy of clean energy utilization.

Vibration-based damage detection in a wind turbine blade through operational modal analysis under wind excitation

In the present study, a vibration-based structural health monitoring (SHM) method for wind turbine blades using Operational Modal Analysis (OMA) is presented. To simulate the dynamic response of the wind turbine, the NREL FAST tool implemented in QBlade was used to calculate the forces in the time domain along the blade in response to the stochastic excitation of the rotating blade by means of the rotationally sampled turbulence spectrum of the wind. These force time series are used as input to the transient analysis in ANSYS Workbench. The acceleration time series extracted along the blade are used for Operational Modal Analysis, and the modal parameters are obtained with the Frequency Domain Decomposition (FDD) algorithm. The comparison between the modal properties of an undamaged and a damaged blade is used for the detection of longitudinal crack-type damage features in the upper part of the blade. The proposed method is shown to be capable of correctly detecting and locating the damage with blade accelerations occurring due to the stochastic excitation by the wind only. This approach could pave the way to low-cost structural health monitoring of wind turbine blades and rotors.

Global optimisation approach for designing high-efficiency piezoelectric beam-based energy harvesting devices

The paper proposes a novel methodology for developing high-power energy harvesting gravity-based devices using an array of piezoelectric beams for wind energy applications. The methodology incorporates a global multidimensional constrained optimisation algorithm, which accounts for the physical size of the device, the physical, geometrical and electrical properties of the piezoelectric beams, and the power management circuit to increase the device’s efficiency. As the beams are plucked sequentially, they vibrate out-of-phase, which consequently leads to charge cancellation issues. The paper proposes and incorporates an electrical circuit design to avoid such problems, being able to further increase the efficiency of the device by 35% when compared against the output from the standard energy harvesting (SEH) circuit with independent rectifiers. The proposed optimisation methodology is applied to the devices utilising flexible polyvinylidene fluoride beams. The developed dynamic numerical model of the beams’ vibration is validated using the experimental results and the results of a Finite Element Analysis. To study the electro-mechanical coupling of the beams, an electric circuit and the power management circuit are created and modelled in Matlab/Simulink software. The optimised device delivers 6 to 17 times higher energy output compared to the unoptimised device. The performance of this device was also compared to that of the device with much stiffer LiNbO3 beams (Clementi et al., 2021, [1]) to demonstrate the direct applicability of such devices to power sensors and transmitter units for structural monitoring of wind turbine blades. It has been demonstrated that the LiNbO3-beam device yields an energy output with one order of magnitude higher. The applied optimisation methodology enabled a 0.057 × 0.017 × 2 m3 dimension device to produce a power output in the range from 0.5 to 1 W depending on the blades’ speed, resulting in 1.06 mW/cm3 power density of the device.

A generalized computational approach to predict high-frequency acoustic pressure response of cavity structures for structural health monitoring of wind turbine blades

This paper details the development of a generalized computational approach that enables prediction of cavity-internal sound pressure distribution due to flow-generated noise at high frequencies. The outcomes of this research is of particular interest for development of an acoustics-based structural health monitoring system for wind turbine blades. The methodology builds from existing reduced-order aeroacoustic modeling techniques and ray tracing based geometrical acoustics and is demonstrated on the model NREL 5 MW wind turbine blade as a case study. The computational predictions demonstrated that damage could be successfully detected in the first half of the blade cavity near the root and that the change in frequency content may be indicative of the type of damage that has occurred. This study provides a foundation to analyze specific blades and likely damage cases for determining key factors of system design such as number and placement of sensors as well as for hardware selection.

Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function

Wind turbine blades are critical components in wind energy generation, and blade health management is a challenging issue for the operation and maintenance of wind turbines. In this paper, an adaptive method is developed to identify blade damages based on the microphone array and compressive beamforming, and global and remote health assessment can be accomplished. In this method, the generalized minimax-concave penalty function is employed to enhance sparse recovery capacities, and step-sizes in computation processes are adjusted adaptively to adapt to variational conditions. Besides, potential damage locations are extracted in coarse acoustic maps to improve convergence rates. Numerical simulations show that high spatial resolutions can be achieved by the proposed method, and the computation time for solving acoustic inverse problems is less than using existing algorithms, especially with low-frequency sources. Moreover, experiments are conducted with a small-scale wind turbine. Results demonstrate that several damages in operating blades can be precisely recognized with high efficiencies, and the deterioration of acoustic maps induced by improper step-sizes can be avoided. The proposed method provides a promising way for in-situ health monitoring of wind turbine blades.

Investigation of interactions between high pulsed ultraviolet lasers and composite graphene/AgNWs films

To improve the joining strength and electrical conductivity, this study aims to develop an ultraviolet (UV) laser annealing to join AgNWs coated on polyethyene terephthalate substrates. The developed graphene/AgNWs-based strain sensors with UV laser-ablated electrodes were highly promising for in situ structural health monitoring of wind turbine blades. After the UV laser annealing, the rate of resistance changes of laser-annealed AgNWs had a minimum value of −2.92 ± 1.0 % when the laser power set at 0.4 W. Moreover, the sheet resistances of laser-ablated graphene/AgNWs films increased with increasing the laser areal fluences. The laser areal fluence of 10.88 J/cm2, pulse repetition frequency of 200 kHz, scan velocity of 400 mm/s, and line scan pitch of 0.02 mm were used to ablate the graphene/AgNWs electrode structure of strain sensors. During the bending test, the graphene/AgNWs-based strain sensors with an electrode width of 0.1 mm had the largest gauge factor of 13.47. Furthermore, the graphene/AgNWs-based strain sensors demonstrated an excellent response and recovery during the real-time bending test of wind turbine blades.

Prediction of wind turbine blade trailing edge noise under various flow conditions for a passive damage detection system

Noise generated by turbulent boundary layer over the trailing edge of a wind turbine blade under various flow conditions is predicted and analyzed for structural health monitoring purposes. Wind turbine blade monitoring presents a challenge to wind farm operators, and an in-blade structural health monitoring system would significantly reduce O&M costs. Previous studies into structural health monitoring of blades have demonstrated the feasibility of designing a passive detection system based on monitoring the flow-generated acoustic spectra. A beneficial next step is identifying the robustness of such a system to wind turbine blades under different flow conditions. To examine this, a range of free stream air velocities from 5 m/s to 20 m/s and a range of rotor speeds from 5 rpm to 20 rpm are used in a reduced-order model of the flow-generated sound in the trailing edge turbulent boundary layer. The equivalent lumped acoustics sources are predicted based on the turbulent flow simulations, and acoustic spectra are calculated using acoustic ray tracing. Each case is evaluated based on the changes detected when damage is present. These results can be used to identify wind farms that would most benefit from this monitoring system to increase efficiency in deployment of turbines.

Reliable packaging of optical fiber Bragg grating sensors for carbon fiber composite wind turbine blades

A sensing module composed of a carbon fiber reinforced polymer (CFRP) packaging and an embedded fiber Bragg grating (FBG) sensor is proposed for strain monitoring of wind turbine blades. The sensing unit is designed and manufactured with the aim of maximizing the strain transfer from a CFRP wind blade to the FBG sensor. The performance of the packaged FBG sensors is experimentally investigated through exhaustive aging and fatigue tests. Then, a series of CFRP-packaged FBG sensors is used to monitor a full-scale 56.85 m-long wind blade during flapwise and edgewise fatigue tests over a few million dynamic strain cycles. Results demonstrate that the independent sensing modules provide efficient strain transfer, verifying a longer lifetime and more reliable measurements compared to conventional electrical strain gauges.

Design of a management system of packaged FBG sensor based on RFID for wind turbine blade

With the application of fiber bragg grating (FBG) sensors in the condition monitoring of wind turbine blades, the layout management of FBG sensors with different central wavelengths and manufacturing processes has attracted much attention. A management system of FBG sensor based on RFID technology is proposed. The management system includes three parts: packaging process of FBG sensor with RFID chip, RFID chip stored information and the FBG sensors status monitoring system, the FBG sensor installation on full scale field wind turbine blade and its maintenance. The proposed management system introduces RFID technology into the blade monitoring using FBG sensor. It provides effective digital management method for the manufacturing, the layout, the installation and the maintenance process of the FBG sensor on the wind turbine blade.

Damage Mechanism Based Approach to the Structural Health Monitoring of Wind Turbine Blades

A damage mechanism based approach to the structural health monitoring of wind turbine blades is formulated. Typical physical mechanisms of wind turbine blade degradation, including surface erosion, adhesive fatigue, laminate cracking and in some cases compressive kinking and failure are reviewed. Examples of a local, damage mechanism based approach to the structural health monitoring of wind turbine blades are demonstrated, including the monitoring of leading edge erosion of wind turbine blades, adhesive bond failure, plydrop delamination, static and dynamic plydrop tests, and bolt and laminate fatigue. The examples demonstrate the possibilities of monitoring specific damage mechanisms, and specific localizations of wind turbine blades.

Integrating Structural Health Management with Contingency Control for Wind Turbines

Maximizing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. In that context, systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage to the turbine. Advanced contingency control is one way to enable autonomous decision-making by providing the mechanism to enable safe and efficient turbine operation. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency control to balance the trade-offs between maintaining system health and energy capture. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.

An adaptive wavelet packet denoising algorithm for enhanced active acoustic damage detection from wind turbine blades

The development of a viable structural health monitoring (SHM) technology for the operational condition monitoring of wind turbine blades is of great interest to the wind industry. In order for any SHM technology to achieve the technical readiness and performance required for an operational implementation, advanced signal processing algorithms need to be developed to adaptively remove noise and retain the underlying signals of interest that describe the damage-related information. The wavelet packet transform decomposes a measured time domain signal into a time-frequency representation enabling the removal of noise that may overlap with the signal of interest in time and/or frequency. However, the traditional technique suffers from several assumptions limiting its applicability in an operational SHM environment, where the noise conditions commonly exhibit erratic behavior. Furthermore, an exhaustive number of options exist when selecting the parameters used in the technique with limited guidelines that can help select the most appropriate options for a given application. Appropriately defining the technique tends to be a daunting task resulting in a general avoidance of the approach in the field of SHM.This work outlines an adaptive wavelet packet denoising algorithm applicable to numerous SHM technologies including acoustics, vibrations, and acoustic emission. The algorithm incorporates a blend of non-traditional approaches for noise estimation, threshold selection, and threshold application to augment the denoising performance of real-time structural health monitoring measurements. Appropriate wavelet packet parameters are selected through a simulation considering the trade-off between signal to noise ratio improvement and amount of signal energy retained. The wavelet parameter simulation can be easily replicated to accommodate any SHM technology where the underlying signal of interest is known, as is the case in most active-based approaches including acoustic and wave-propagation techniques. The finalized adaptive wavelet packet algorithm is applied to a comprehensive dataset demonstrating an active acoustic damage detection approach on a ~46 m wind turbine blade. The quality of the measured data and the damage detection performance obtained from simple spectral filtering is compared with the proposed wavelet packet technique. It is shown that the damage detection performance is enhanced in all but one test case by as much as 60%, and the false detection rate is reduced. The approach and the subsequent results presented in this paper are expected to help enable advancement in the performance of several established SHM technologies and identifies the considered acoustics-based SHM approach as a noteworthy option for wind turbine blade structural health monitoring.

An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades

This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.