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Damage monitoring is an essential step to understand fatigue damage growth in composite wind turbine blades and for reliable lifetime prediction. The current study is an experimental investigation of fatigue delamination induced by an intentionally embedded defect in the spar cap of a 31 m wind turbine blade. A constant cyclic flap-wise bending was applied to the blade for 320,000 cycles. Delamination growth is identified and measured by visual inspection, acoustic emission (AE), and infrared thermography of the blade surface. It was observed that the area of delaminated regions grew faster during the early cycles, then their growth rates decreased, and the interlayer cracks reached stable growth. The experiment shows fatigue delamination develops gradually in the spar cap and allows monitoring of the damage before reaching a critical stage. The ability of AE and thermography methods to detect subsurface damage is demonstrated by the identification of steady delamination growth during cyclic load. Damage localization by both methods is in good agreement with the delamination location. Most acoustic activities are spotted in the boundaries of delaminated regions, and the position of a significant number of acoustic activities with the highest energy content correlates with the location of delamination crack fronts. It is shown that the surface temperature distribution contour indicates the shape of the largest delaminated region, and the growth of the hot region area correlates with damage propagation. However, multiple delaminations through the thickness cannot be discriminated from the thermal images. [ABSTRACT FROM AUTHOR]

Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness. [ABSTRACT FROM AUTHOR]

A significant problem associated with the implementation of Vibration-Based Structural Health Monitoring (VSHM) systems originates from the detrimental effects caused by Environmental and Operational Variations (EOVs). The EOVs cause observations from the same structural condition to behave in different manners. As such, this leads to issues when defining a robust baseline state as well as making the discrimination between undamaged and damaged observations more challenging. In order to address these challenges, multivariate nonlinear regression is implemented to account for the EOVs. Damage Sensitive Features (DSFs) are extracted from acceleration data and then are regressed based on environmental and operational parameters. New features are effectively normalised by finding the difference between the measured and predicted values. This process removes the influence of the EOVs in an explicit manner, allowing for more reliable damage detection. While the benefit of the application of different regression methodologies has already been demonstrated in the past, this work addresses a number of practicalities in the implementation of VSHM on real systems. The analysis investigates the selection of the DSF and is investigated alongside another analysis into how the damage detection behaves under varying amounts of input information. Furthermore, a method is proposed to understand and account for a large number of outliers in the training data. [ABSTRACT FROM AUTHOR]

The study aims at damage detection on the blade of an operating Vestas V27 wind turbine via a single vibration response sensor under varying Environmental and Operating Conditions (EOCs), and through this, at exploring the performance limits of robust vibration-based Statistical Time Series type methods. Three trailing edge progressive, 15 cm, 30 cm, and 45 cm long, damage scenarios are examined using signals from a 104-day-long measurement campaign. The study is based on three robust methods: an Unsupervised Principal Component Analysis AutoRegressive model–based (U-PCA-AR) method, an Unsupervised Multiple Model AutoRegressive model–based (U-MM-AR) method, and an Unsupervised Principal Component Analysis Multiple Model AutoRegressive model–based (U-PCA-MM-AR) method. Comparisons with a nominal Unsupervised AutoRegressive model–based (U-AR) method and an 8-sensor–based method are also reported. The results of the study are based on a systematic and thorough assessment procedure using 7000 inspection experiments and confirm that single-sensor-based detection is indeed feasible via the aforementioned robust methods, with the best performance achieved by the U-PCA-MM-AR method which reaches up to 100% True Positive Rate at 4%, 1%, and 0% False Positive Rate for the 15, 30, and 45 cm damage scenarios, respectively. This performance is on par with that of the 8-sensor–based method and indicative of the high capabilities offered by the robust vibration-based Statistical Time Series type methods. The sensitivity of the methods with respect to the sensor location and the AutoRegressive model order is also examined. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and efficiency of wind energy generation, and it is a challenging issue due to the complicated structures and variational operating conditions. In this investigation, a SHM method for wind turbine blades based on the microphone array and acoustic source identification is proposed. With the equipment of loudspeakers in blade cavities, damage-related information is excited to be captured by the array. To generate accurate acoustic maps with high spatial resolutions, a novel algorithm for sparsity-based sound field reconstruction is developed based on the generalized minimax-concave penalty function. With a laboratory-scale wind turbine model, damage identification performance of the proposed method is evaluated under different parametric and measuring conditions, and experiments are conducted under diverse blade health conditions. Results reveal that and both internal and external damage in operating blades can be recognized as acoustic sources, and satisfactory performance of the proposed method can be guaranteed with appropriate parameters. Furthermore, determination criteria for parameters are concluded with respect to the variation of measuring conditions. This prototype study provides useful insights into the development of effective SHM systems. [ABSTRACT FROM AUTHOR]

Blades play a critical role in the wind turbine system. Therefore, their structural health monitoring is very important. Blades are damaged by sudden changes in wind load, cracks due to collision of foreign objects, and disasters, such as lightning strikes, hail, and typhoons. Moreover, blades are expensive to maintain. Defects or damages to wind turbine blades reduce the life span and power generation efficiency of the wind turbine and increase safety risks and maintenance costs. Therefore, it is very important to detect blade damage to prevent problems in the wind turbine. Ultrasonic inspection is suitable for blades made of composite materials. Piezoelectric ceramic, which is a typical piezoelectric element, has relatively high sensitivity compared to other sensors. However, it suffers from brittle fractures and thus difficult to apply to curved structures. To overcome the limitations of piezoelectric ceramics, a piezoelectric flexible line sensor that can be applied to curved surfaces was manufactured using the dice-and-fill method for a [Pb(Li0.25Nb0.75)]0.06 [Pb(Mg0.33Nb0.67)]0.06 [Pb(Zr0.50Ti0.50)]0.88O3 with 0.7 wt% MnO2 (PZTNMML) ceramic disc. Instead of a typical ultrasonic inspection method with limited surface contact, a laser capable of producing ultrasonic excitation of ultrasonic waves over a large area from a long distance was used. The possibility of detecting a defect on the wind turbine blade using a piezoelectric flexible line sensor and laser ultrasound was confirmed in this study. [ABSTRACT FROM AUTHOR]

In the far north, low temperatures and atmospheric icing are a major danger for the safe operation of wind turbines. It can cause several problems in fatigue loads, the balance of the rotor and aerodynamics. With the aim of improving the rigidity of the wind turbine blade, composite materials are currently being used. A numerical work aims to evaluate the effect of ice on composite blades and to determine the most adequate material under icing conditions. Different ice thicknesses are considered in the lower part of the blade. In this paper, modal analysis is performed to obtain the natural frequencies and corresponding mode shapes of the structure. This analysis is elaborated using the finite element method (FEM) computer program through ABAQUS software. The results have laid that the natural frequencies of the blade varied according to the material and thickness of ice and that there is no resonance phenomenon. [ABSTRACT FROM AUTHOR]

The development of two novel elastomeric erosion resistant coatings for the protection of wind turbine blades is presented. The coatings are prepared by modifying polyurethane (PU) with (i) hydroxyl functionalised graphene nanoparticles (f-GNP) and (ii) f-GNP and a hydrophobic silica-based sol–gel (SG). Tensile, monotonic and cyclic compression and tearing tests have been conducted on the neat PU and the two newly developed elastomeric PU nanocomposites (PU + GNP and PU + GNP + SG) to allow their properties to be compared. The test results showed that the mechanical properties of PU and the modified PUs have strong dependency on temperature, strain rate and nanoparticles loading and addition of GNP and SG to PU improved the mechanical properties. Compared to PU, Young's modulus and modulus of toughness of PU + GNP + SG increased 95% and 124%, respectively. The PU + GNP nanocomposite displayed the highest tearing strength and the PU + GNP + SG nanocomposite showed the highest elongation at break. An investigation of the microstructures of the modified PUs by FTIR, field emission scanning electron microscope (FESEM) and energy-dispersive X-ray spectroscopy (EDX) are discussed. Hydrophobicity of the PU and developed PU nanocomposites are reported by measuring their water droplet contact angles and their free surface energies. [ABSTRACT FROM AUTHOR]

For investigation of the effects of turbulence intensity (TI) on the wind loads on wind turbine blade, a 1:20 scaled model of a typical 3D wind turbine blade is designed and used for the pressure measurement test in a wind tunnel. Five uniform flows with different turbulence intensities are simulated in the wind tunnel test. The mean and root-mean-square (RMS) wind pressure coefficients, base moment coefficients, and their power spectral densities are presented and discussed in detail. Combined with the dynamic properties of the blade structure, wind-induced displacements at the tip of the blade are calculated by the random vibration theory. The results show that the increasing of TI amplifies the aerodynamic loads on the blade in terms of RMS wind pressure coefficients and RMS bending moment coefficients. Large wind-induced displacement of the wind turbine blade may be stimulated by high TI even under the feathering condition. This article aims to further the understanding of wind loads on wind turbine blades and provide useful information for the wind-resistant design of wind farms established in regions with high turbulence levels. [ABSTRACT FROM AUTHOR]

This article first presents a description of the different load types to which a wind turbine blade is subjected. Analytical equations are derived to express blade loads from operation parameters of the wind turbine (rotor and nacelle velocities and accelerations; pitch, coning, tilt, and azimuth angles; blade mass properties; turbine geometry). This allows a better understanding of the contribution of each of these parameters to the total load on a blade. A difficulty arises for transferring the loads computed by an aeroelastic model (a one-dimensional model of the blade) to a three-dimensional finite element model of the blade. A method is proposed for that purpose. It consists in applying the aerodynamic loads using RBE3 elements and applying gravitational and inertial loads as volume forces. Finally, an example of this method used for the design of a 10 kW wind turbine blade is presented. [ABSTRACT FROM AUTHOR]

This article studies experimentally the damage behaviors of a 59.5-m-long composite wind turbine blade under accelerated fatigue loads using acoustic emission technique. First, the spectral analysis using the fast Fourier transform is used to study the components of acoustic emission signals. Then, three important objectives including the attenuation behaviors of acoustic emission waves, the arrangement of sensors as well as the detection and positioning of defect sources in the composite blade by developing the time-difference method among different acoustic emission sensors are successfully reached. Furthermore, the clustering analysis using the bisecting K-means method is performed to identify different damage modes for acoustic emission signal sources. This work provides a theoretical and technique support for safety precaution and maintaining of in-service blades. [ABSTRACT FROM AUTHOR]

Wind energy is one among the most promising renewable energy sources, and hence there is fast growth of wind energy farm implantation over the last decade, which is expected to be even faster in the coming years. Wind turbine blades are complex structures considering the different scientific fields involved in their study. Indeed, the study of blade performance involves fluid mechanics (aerodynamic study), solids mechanics (the nature of materials, the type of solicitations ...), and the fluid coupling structure (IFS). The scope of the present work is to investigate the mechanical performances and structural integrity of a large offshore wind turbine blade under critical loads using blade element momentum. The resulting pressure was applied to the blade by the use of a user subroutine "DLOAD" implemented in ABAQUS finite element analysis software. The main objective is to identify and predict the zones which are sensitive to damage and failure as well as to evaluate the potential of composite materials (carbon fiber and glass fiber) and their effect on reduction of rotor's weight, as well as the increase of resistance to wear, and stiffness. [ABSTRACT FROM AUTHOR]

A novel structural damage detection methodology that relies on the detectability of the changes in acoustic transmissibility across boundaries of structural cavities is investigated. The approach focuses on active damage detection by leveraging the acoustic pressure responses measured external to structural cavities while exposed to internal acoustic excitations. The active damage detection concept is first demonstrated on a 4 m wind turbine blade using acoustic beamforming techniques to confirm that the acoustic energy transmitted through a damaged surface increases local to the damage compared to an undamaged surface. The concept is further verified, only considering acoustic pressure responses measured from limited microphones positioned at various distances from a ~46 m wind turbine blade. A comprehensive testing campaign is developed and executed on the utility-scale blade considering various damage types, severity levels, and locations. The data are analyzed using a combination of spectral analysis and statistics-based metrics to detect and track the progression of damage as well as identify trends across the test variables. Overall, large increases in the power spectral density were observed from the pressure responses measured external to the structure in most cases. The spectral differences increased as the damage became more severe and damage as small as 5.1 cm in length was easily detected from multiple sensors up to 17.1 m from the damage location. Damage was easily detected when implemented before the mid-length of the blade using simple signal processing algorithms and preliminary test configurations. The data acquired in this work serve as a preliminary investigation into the capability of the approach on complex structures and paves the path for future research into the signal processing techniques and test configurations that will enhance the performance of the active acoustic damage detection approach. [ABSTRACT FROM AUTHOR]

The aim of this work is to provide insights into the advantages and the limitations of the extension of a strong viscous–inviscid interactive code for modeling the effects of vortex generators, with focus on the calibration and validation for wind turbine airfoils. The proposed methodology relies on the approach proposed by other authors in the past and introduces an alternative formulation for the lag dissipation within the integral boundary-layer equations whose effects on the numerical prediction are evaluated. Besides the verification of the aeronautical test cases, particular attention is devoted to the validation of the results for several airfoils commonly used in wind turbine design. A blind test for airfoils from an industrial test case is conducted as well. Results show that the maximum lift coefficient can be well predicted by the present implementation, whereas the angle of maximum lift is a little higher than in wind tunnel measurements. Apart from that, the drag coefficients of airfoils with vortex generators cannot be predicted by the current implementation, but it is expected that the maximum lift and the stall angle of attack are more important in the design phase, since they determine power, loads, and noise. [ABSTRACT FROM AUTHOR]

It is common to dissociate load computation from structural analysis when carrying out a numerical assessment of a wind turbine blade. Loads are usually computed using a multiphysics and multibody beam finite element model of the whole turbine, whereas detailed structural analysis is managed using shell finite element models. This raises the issue of the application of the loads extracted from the beam finite element model at one node for each section and transposed into the shell finite element model. After presenting the methods found in the literature, a new method is proposed. This takes into account the physical consistency of loads: aerodynamic loads are applied as pressure on the blade surface, and inertial loads are applied as body loads. Corrections imposed by pressure and body load computation in order to match loads from the beam finite element model are proposed and a comparison with two other methods is discussed. [ABSTRACT FROM AUTHOR]

An efficient ice detection system is an important tool to optimize the de-icing processes in wind turbines operating in cold climate regions. The aim of this work is to study the application of guided wave for ice detection on wind turbine blades. Computational model is developed to simulate guided wave propagation on composite structures. The model has been validated with experimental data obtained in cold climate laboratory. Effect of ice accretion on composite structures is studied in the time, frequency and wavenumber domains. In each case, post-processing algorithms as well as icing index are introduced which are sensitive to accumulated ice on the composite structure. The algorithms and icing index are applied to both simulation results and experimental data. Analysis of the obtained results has shown that the guided wave–based approach can be used for developing ice detection systems for wind turbine blades. [ABSTRACT FROM AUTHOR]

This work demonstrates a technique to identify information about the ice mass accumulation on wind turbine blades using its natural frequencies, and these frequencies reduce differently depending on the spatial distribution of ice mass along the blade length. An explicit relation to the natural frequencies of a 1-kW wind turbine blade is defined in terms of the location and quantity of ice mass using experimental modal analyses. An artificial neural network model is trained with a data set (natural frequencies and ice masses) generated using that explicit relation. After training, this artificial neural network model is given an input of natural frequencies of the iced blade (identified from experimental modal analysis) corresponding to 18 test cases, and it identified ice masses’ location and quantity with a weighted average percentage error value of 17.53%. The proposed technique is also demonstrated on the NREL 5-MW wind turbine blade data. [ABSTRACT FROM AUTHOR]

In this study, the I-beam structure of a utility-scale blade is used as the basis for the design of a small-scale subcomponent emulating the same type of structural element of a utility-scale wind turbine blade. Governing equations for the bending of a shear deformable thin-walled composite I-beam are considered. Similitude theory is applied to derive the corresponding scaling laws. Accuracy of the derived scaling laws in predicting the strain and displacement fields of the full-scale I-beam is considered as a design criterion for small-scale I-beams. Both complete and partial similarity cases are discussed. A novel approach is proposed to design partially similar scaled I-beams with totally different layups from those of the full-scale I-beam. Using the proposed technique, scaled-down composite I-beams are designed that predict the strain and displacement fields of their full-scale parent I-beam with very good accuracy. [ABSTRACT FROM AUTHOR]

This article proposes a non-contacting measurement technique based on acoustic monitoring to detect cracks or damage within a structure by observing sound radiation using a single microphone or a beamforming array. The technique works by mounting an audio speaker inside a hollow structure, such as a wind turbine blade, and observing the sound radiated from the blade to identify damage. The primary hypothesis for this structural damage detection technique is that the structural damage (cracks, edge splits, holes, etc.) on the surface results in changes in the sound radiation characteristics of the structure. Preliminary measurements to validate the methodology were carried out on a section of a wind turbine blade containing different sized holes and cracks. An acoustic microphone array with 62 microphones was used to measure the sound radiated from the structure when an audio speaker generating random noise was placed inside a cavity emulating a wind turbine blade. A phased array beamforming technique and CLEAN-based subtraction of point spread function from a reference were employed to locate the different damage types on the test structures. The same experiment was repeated using a commercially available 48-channel acoustic ring array to compare the test results. It was shown that both the acoustic beamforming and the CLEAN-based subtraction of point spread function from reference techniques can identify the damage in the test structures with sufficiently high fidelity. [ABSTRACT FROM AUTHOR]

Wind blades are major structural elements of wind turbines, but they are prone to damage like any other composite component. Blade damage can cause sudden structural failure and the associated costs to repair them are high. Therefore, it is important to identify the causation of damage to prevent defects during the manufacturing phase, transportation, and in operation. Generally, damage in wind blades can arise due to manufacturing defects, precipitation and debris, water ingress, variable loading due to wind, operational errors, lightning strikes, and fire. Early detection and mitigation techniques are required to avoid or reduce damage in costly wind turbine blades. This article provides an extensive review of viable solutions and approaches for damage mitigation in wind turbine blades. [ABSTRACT FROM AUTHOR]

Structural health monitoring offers an attractive tool for condition assessment, fault prognosis and life-cycle management of wind turbine components. However, owing to the intense loading conditions, geometrical nonlinearities, complex material properties and the lack of real-time information on induced structural response, damage detection and characterization of structural components comprise a challenging task. This study is focused on the problem of damage detection for a small-scale wind turbine (Sonkyo Energy Windspot 3.5 kW) experimental blade. To this end, the blade is dynamically tested in both its nominal (healthy) condition and for artificially induced damage of varying types and intensities. The response is monitored via a set of accelerometers; the acquired signals serve for damage detection via the use of appropriate statistical and modal damage detection methods. The former rely on extraction of a characteristic statistical quantity and establishment of an associated statistical hypothesis test, while the latter rely on tracking of damage-sensitive variations of modal properties. The results indicate that statistical-based methods outperform modal-based ones, succeeding in the detection of induced damage, even at low levels. [ABSTRACT FROM AUTHOR]

Understanding local inflow conditions on a wind turbine blade on an operating wind turbine can further understanding of aerodynamic variations and help predict loads on the turbine. Turbine blades are generally designed with a two-dimensional steady approach, however the real wind conditions are highly three-dimensional (3D) and unsteady. Detailed measurements are not common for validating aerodynamic models. The aim of this theoretical and experimental study is to build, calibrate, install and test a compact inblade five hole pressure probe system to be used to retrieve these measurements. Wind tunnel calibration of the five hole pressure probe has been successfully completed using an automated traversing system over a ±45° range, with 5° increments. Error analysis showed that the multi-zone pressure coefficient data reduction approach is the most suitable for this application. This approach not only extends the measurable local inflow angles up to ±70°, but also it allows any reference pressure for differential pressure readings. A new data acquisition system (DAQ) internal to a rotating small wind turbine blade section was developed. Space limitations resulted in a custom built DAQ of very contained dimensions. This included, among others, five pressure transducers on a printed circuit board, a 16 bit analog to digital converter, an Arduino microcontroller, and a Bluetooth transceiver to transmit the data wirelessly to the main computer. The new blade section was designed and 3D-printed in such a way that the DAQ instrumentation could be easily accessed and, at the same time, had an acceptable structural solidity. A series of tests were conducted on a 3.4 m diameter wind turbine in a large scale wind tunnel in order to assess the correct functioning of the probe system. As expected, the inflow measurement obtained while the turbine was operating under yawed conditions showed a periodically oscillating inflow vector. The period of this variation was the same as the period of the rotor rotation. [ABSTRACT FROM AUTHOR]

In this article, the fatigue strength in composite materials of wind turbine blades under dry-wet conditions is predicted using artificial neural networks. Compression-compression constant amplitude fatigue tests were performed on thermoset polymer resins including polyesters and vinyl esters. Coupons were tested under an air temperature of 20°C and 50°C in both "dry" and "wet" conditions. The results show that artificial neural network can provide accurate fatigue strength prediction for different resin matrices under different values of temperature. [ABSTRACT FROM AUTHOR]

The research presented in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. This article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure. [ABSTRACT FROM AUTHOR]

A mathematic rotating blade model is introduced in conjunction with periodic time-varying aerodynamic load, which is described by Beddoes-Leishman dynamic stall model. The consequent aeroelastic model is utilized to analyze blade dynamics and design control strategy for blade flutter suppression application. Aeroelastic stability of rotating blade is indicated by Floquet Theory, and the stability indications show good agreement with results of open-loop simulation test for critical flutter speed study. It's shown that the Adaptive Controller is capable of restraining flutter vibration with the actuation of the trailing-edge flap. The robustness and effectiveness of the controller are shown by closed-loop tests with a wide range of aerodynamic loads. The stability analysis proves the stability of the Adaptive Controller by the Adaptive Stability Theorem. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates the use of the Absolute Nodal Coordinate Formulation (ANCF) in modeling large size wind turbine blades. Modern blade model can be divided into two regions classified by aerodynamic and structural function. The aerodynamic region, blade-span, is utilizing the thinnest possible airfoil section. On the other hand, the transition between the circular mount and the first airfoil profile is referred as blade-root region, which carries highest loads along the blade. In this investigation, an efficient procedure is developed for mapping NACA airfoil wind-turbine blades into ANCF thin plate models. The procedure concerns a complete wind turbine blade structure, blade-root as well as the blade-span regions with non-uniform and twisted nature. As a result, the slope discontinuity problem arises in both chord-wise and span-wise directions, and consequently presents numerical errors in dynamic simulation. The paper investigates the methods of modeling slope discontinuity resulting from the variations of the cross-sectional layouts across the blade. The developed method is applied for the gradient-deficient thin plate element in order to account for structural discontinuity. In addition, the aerodynamic loads are precisely expressed and the aerodynamic characteristics of such blades are examined with the ANCF and with the classical finite element method. The static and dynamic solutions of different operating conditions are obtained and results are compared with those obtained using ANSYS code. Both the limitations and advantages of using the ANCF in modeling large size wind turbine blades are concluded and discussed. A Dynamics for Design (DFD) procedure is presented with numerical example concerning large-rotation, large deformation wind turbine blades. [ABSTRACT FROM PUBLISHER]

Rapid growth in generating power from wind turbines led to application of long laminated composite blades. However, as the length of blades increases, the risk of failure due to excessive vibration increases. Therefore, a reliable wind power generator requires an efficient and accurate, yet economical structural health monitoring system to detect vibration and apply a reliable control to prevent from unpredicted failure. This paper presents an optimal, low cost and continuous vibration monitoring system for laminated composite blades using Fiber Bragg Grating (FBG). Utilizing a layerwise displacement theory, the Finite Element (FEM) model has been developed for the wind turbine blade. The optomechanical relations between strains and FBG wavelength shift have been presented to study the dynamic of a wind turbine blade. The reflected spectrum caused by modal displacement, interrogated by WDM method has been studied for vibration control. Numerical examples have been presented to compare the FEM and FBG results which show a very good agreement for the first 3 vibration modes. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) of a wind turbine blade using a Structural Neural System (SNS) is described in this paper. Wind turbine blades are composite structures with complex geometry and sections that are built of different materials. The 3D structure, large size, anisotropic material properties, and the potential for damage to occur anywhere on the blade makes damage detection a significant challenge. A SNS based on acoustic emission (AE) monitoring (passive listening) was developed for practical low cost SHM of large composite structures such as wind turbine blades. The SNS was tested to detect damage initiation and propagation on a 9 m long wind turbine blade during a quasi-static proof test to failure at the National Renewable Energy Laboratory test facility in Golden, Colorado. Twelve piezoelectric sensors were bonded on the surface of the wind turbine blade and connected to form four continuous sensors which were used in the SNS to determine damage locations. Although 12 sensors monitored the wind turbine blade, the SNS produces only two analog output signals; one time signal to determine and locate damage, and a second time signal containing combined AE waveforms. Testing of the wind turbine blade produced some interesting results. After initial emissions due to settling of the blade diminished, damage initiated at one location on the blade. As the load was increased, damage occurred in a sequence at three other locations until there was a catastrophic buckling failure of the blade. The buckling occurred above the design load for the blade, and was due to the carbon spar cap disbonding from the fiberglass shear web under compressive bending stress. The SNS indicated the general area where the damage started and how the damage progressed, which is valuable information for verifying and improving the blade design and the manufacturing procedure. Strain gages on the blade did not provide a clear indication of damage until buckling occurred. A major outcome of this testing was to provide confidence that SHM of large composite structures that have complex geometry and multiple materials is practical using a simple, low cost SNS. [ABSTRACT FROM PUBLISHER]