Monitoring Of Wind Turbine Blades Research Articles

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.

A computational investigation of airfoil aeroacoustics for structural health monitoring of wind turbine blades

AbstractA generalized computational methodology for reduced order acoustic‐structural coupled modeling of the aeroacoustics of a wind turbine blade is presented. This methodology is used to investigate the acoustic pressure distribution in and around airfoils to guide the development of a passive damage detection approach for structural health monitoring of wind turbine blades for the first time. The output of a k − ε turbulence model computational fluid dynamics simulation is used to calculate simple acoustic sources on the basis of model tuning with published experimental data. The methodology is then applied to a computational case study of a 0.3048‐m chord NACA 0012 airfoil with two internal cavities, each with a microphone placed along the shear web. Five damage locations and four damage sizes are studied and compared with the healthy baseline case for three strategically selected acoustic frequencies: 1, 5, and 10 kHz. In 22 of the 36 cases in which the front cavity is damaged, the front cavity microphone measures an increase in sound pressure level (SPL) above 3 dB, while rear cavity damage only results in six out of 24 cases with a 3‐dB increase in the rear cavity. The 1‐ and 5‐kHz cases show a more consistent increase in SPL than the 10‐kHz case, illustrating the spectral dependency of the model. The case study shows how passive acoustic detection could be used to identify blade damage, while providing a template for application of the methodology to investigate the feasibility of passive detection for any specific turbine blade.

Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

Wind energy is one of the essential renewable energy resources because of its consistency due to the development of the technology and relative cost affordability. The wind energy is converted into electrical energy using rotating blades which are connected to the generator. Due to environmental conditions and large construction, the blades are subjected to various faults and cause the lack of productivity. The downtime can be reduced when they are diagnosed periodically using condition monitoring technique. These are considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and fault classification. In this study, statistical features are extracted from vibration signals, feature selection are carried out using J48 algorithm and the fault classification was carried out using logistic model tree algorithm.

Research on Hyper pipes and Voting Feature Intervals Classifier for Condition Monitoring of Wind Turbine Blades using Vibration Signals

Renewable energy is viewed as a vital energy field due to the present energy devastations. Among the vital substitutions being considered, wind energy is a strong challenger as a result of its reliability. “To yield wind energy more effectively, the structure of wind turbines has designed bigger, making protection and restoration works difficult. Because of different natural conditions, wind turbine blades are exposed to vibration and it prompts failure. If the failure is not analyzed initially, then it will haste dreadful destruction of the turbine structure. To increase safety perceptions, to decrease down time and to cut down the repeat of unpredictable breakdowns, the wind turbine blades must be examined from time to time to guarantee that they are in great condition. In this paper, a three bladed wind turbine was preferred and using vibration source through statistical features, the state of a wind turbine blade is inspected. The fault classification is carried out using machine learning techniques like hyperpipes (HP) and voting feature intervals (VFI) algorithm. The performance of these algorithms is compared and better algorithm is suggested for fault prediction on wind turbine blades.”

Health monitoring of wind turbine blades in operation using three-dimensional digital image correlation

Abstract Wind turbine blades are subjected to fluctuating loads during operation, which makes them vulnerable to reduced performance and mechanical failures. In addition, the maintenance of blades is time-consuming and expensive. In this paper, a novel and economic optical technique based on three-dimensional digital image correlation (3D-DIC) is described for monitoring the health of wind turbine blades. A fault detection method is proposed based on the relative deformation of the turbine blades during operation. To validate the approach, a 5 kw wind turbine with 4 m diameter was evaluated using 3D-DIC. The rotor blades were prepared with a random black-and-white pattern of dots and two digital cameras were located in front of the wind turbine to document the rotor blade deformation. The full-field dynamic parameters of displacement and strain were obtained and a diagnosis of the blade health was conducted in both the time and frequency domains. Results showed that 3D-DIC can serve as an effective non-contact method for monitoring the health of wind turbine blades during operation.

Design of a disk-swing driven piezoelectric energy harvester for slow rotary system application

This paper presents mathematical modelling of an energy harvester that converts slow mechanical rotation into piezoelectric vibration using a small disk and a pair of magnets, for large-scale machinery monitoring applications such as wind turbine blades. The harvester consists of a piezoelectric cantilevered beam, a gravity-induced disk, and magnets attached to both the beam and the disk. The energy method is used to derive the three coupled equations that describe the motion of the disk, the vibration of the beam, and the harvester voltage output. The equations are solved using ODE45 in MATLAB software and verified by the corresponding experimental study. The result shows varied energy harvesting performance by blade rotational speed. At low blade speed, the harvester generates power by regularized magnetic excitation per blade revolution. At high blade speed, however, the disk behavior becomes chaotic to increase excitation force and power generation. The results show that the model can quantify power as a function of blade speed, and the proposed harvester can generate a considerable amount of power for self-sustainable sensing and monitoring of wind turbine blades.

An Overview of Microwave UWB Antenna for Structural Health Monitoring of Wind Turbine Blades: Optimal Design and Analysis

An Overview of Microwave UWB Antenna for Structural Health Monitoring of Wind Turbine Blades: Optimal Design and Analysis

Condition monitoring of wind turbine blades and tower via an automated laser scanning system

During its service life, a wind turbine has to withstand very challenging loads and severe environmental factors which can cause some deteriorations and cracks on structural components. Yearly periodic controls may not be sufficient to diagnose a fault at early stages. Therefore, wind turbines are very important systems which have to be monitored continuously. This work aims at developing an automated scanning system for IR (Infrared) laser vibrometers to conduct the tests and measurements, which are required for condition monitoring, in a more accurate and efficient manner. Unlike visible green or red lasers, IR laser is reflected by most surfaces (especially by blade material) with a very high intensity without needing a preparation or surface enhancement on the target. However, IR laser cannot be guided by refracting/reflecting the beam by lenses and mirrors used by conventional laser scanners. This work proposes a new methodology which is based on mounting the laser source on a motorized platform and then rotating the platform rather than guiding the laser beam by optical systems. The tests performed by using wind turbine models show that the proposed system is capable of guiding the laser to the desired measurement points with very high precision and efficiency. The results of the analyses also show that some important dynamic characteristics of the system such as Eigenfrequencies and mode shapes can be extracted very accurately.

Improvement in wind energy production through condition monitoring of wind turbine blades using vibration signatures and ARMA features: a data-driven approach

The main objective of this study is to improve the wind energy productivity by implementing the condition monitoring technique for wind turbine blades through vibration source. The fault detection and the isolation of the fault which affects the wind energy productivity were carried using machine learning algorithms. In this study, a three bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults which affect the turbine blade. Initially, vibration sources were collected from the wind turbine using piezoelectric accelerometer and from that vibration source; needed features are extracted using ARMA through MATLAB. From the extracted feature, the dominating feature is selected using J48 decision tree algorithm and with the selected features, fault classification has been carried out. The fault classifications were carried out using Bayesian, function and lazy classifiers.

A summary of defect evolution and health monitoring of wind turbine blades

A summary of defect evolution and health monitoring of wind turbine blades

A summary of defect evolution and health monitoring of wind turbine blades

The wind turbine blade is one of most critical systems in terms of failure as the wind turbines are rapidly becoming larger and more complicated. Developing better design procedures are ongoing and the enhancements over the time are promising and requires reliable supportive monitoring and maintenance systems to ensure the satisfactory level of asset cost effectiveness. The purpose of the paper is to review the contributions concerning the wind turbine blade defects, in order to: understand how the defects in wind turbine blades evolve, i.e., initiate and propagate over the lifetime and to understand how the defects in wind turbine blades can be monitored. The paper reviews the mechanical dynamic models, testing and condition monitoring techniques. The paper proceeds to provide a generic defect evolution scenario with aim to enhance the fragmented understanding of the micro and macro-structure behaviour of the wind turbine blades.

Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography

The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface damage but most require close proximity between the sensor and the blade. Thermography, as a non-contact method, may avoid this problem. Both passive and active pulsed and step heating and cooling thermography techniques were investigated for different purposes. A section of a severely damaged blade and a small “plate” cut from the undamaged laminate section of the blade with holes of varying diameter and depth drilled from the rear to provide “known” defects were monitored. The raw thermal images captured by both active and passive thermography demonstrated that image processing was required to improve the quality of the thermal data. Different image processing algorithms were used to increase the thermal contrasts of subsurface defects in thermal images obtained by active thermography. A method called “Step Phase and Amplitude Thermography”, which applies a transform-based algorithm to step heating and cooling data was used. This method was also applied, for the first time, to the passive thermography results. The outcomes of the image processing on both active and passive thermography indicated that the techniques employed could considerably increase the quality of the images and the visibility of internal defects. The signal-to-noise ratio of raw and processed images was calculated to quantitatively show that image processing methods considerably improve the ratios.

Radar‐based structural health monitoring of wind turbine blades: The case of damage localization

AbstractThis short communication reports on a radar approach for structural health monitoring of wind turbine blades. Therefore, a bistatic frequency‐modulated continuous wave (FMCW) radar in the frequency range from 33.4 to 36.0 GHz has been developed and tested experimentally using a laboratory wind turbine demonstrator. A differential damage localization framework is presented here that exploits signal differences between measurements from the intact and the damaged structure for 3D imaging of the defect. We have achieved the localization of a 30‐mm cut in a glass fiber composite structure as well as the localization of a water pack at the backside of the specimen with a localization error of several centimeters.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

Temperature effects on vision measurement system in long-term continuous monitoring of displacement

Videogrammetry has been recognized as a promising method for monitoring the dynamics of wind turbines. On the way forward, the environmental effects on the measurement accuracy of the videogrammetry are key issues should be solved. This study therefore carried out an investigation into the temperature-induced measurement error of the videometric technique in long-term continuous monitoring of displacement. First, videometric measurement tests specifically targeted for improving the applicability of the videogrammetry to wind turbine blade monitoring were conducted. Making use of the measurement data, the characteristics of the temperature-induced measurement errors were obtained by examining them as a whole as well as their wavelet decomposed components. Comprehensive correlation analyses were performed to quantify the degree of correlation between measurement error and temperature change. It is found that the vertical displacement measurement error and temperature change have a satisfactory linear relationship, while the relationship between horizontal displacement measurement error and temperature change is more scattered. Making use of the unique features of the deformation and thermal expansion of a utility-scale wind turbine blade, it is likely to subtract the temperature-induced measurement error from the measured displacement with the help of the relationship between measurement error and temperature change or a time-frequency approach.

A Feasibility Study on CNT- and Graphene-aided Structural Health Monitoring of Wind Turbine Blades

This paper presents a study on incorporation of hybridized MWCNTS and rGO in fiber-reinforced plastics for structural health monitoring in real time. Carbon nanoparticle that has certain MWCNT to rGO ratio was dispersed in a solvent were uniformly spray-coated on the surfaces of glass fiber fabrics, which were then layed-up and impregnated with an unsaturated polyester resin using vacuum-assisted resin transfer molding(VARTM) to form composite samples. Prior to VARTM process, electrodes were embedded on the sprayed coated glass fiber ply for electrical resistance change monitoring. The composite sample was subjected to cantilever bending and twist test, during which the changes in resistances between various electrode pairs were measured and recorded. Experimental results showed the dependence of resistance change on the deformation. In particular, this research covers hybridized MWCNTS and rGO electrical network on wind turbine blade. Cantilever bending and twist test were demonstrated by simulation program and this paper gives proposal about future application on huge composite part with MWCNT and rGO.

Design of a sparse antenna array for radar‐based structural health monitoring of wind turbine blades

The imaging performance of a sparse antenna array depends on the arrangement of the transmitting and receiving elements. In this study, the authors systematically extend the work presented by Caba and Boreman by a constraint optimisation of the Y-shaped antenna arrangement. This will lead to an improved point-spread function with reduced sidelobes. It was found that a 30% improvement of the imaging performance using backprojection techniques could be achieved compared with conventional array designs. The optimised sparse antenna array has been used in a simulation study to evaluate its performance for radar-based structural health monitoring of wind turbine blades. Depending on the damage position, either close to the front or back side of the rotor blade, the localisation error has been quantified as a function of its generally unknown effective relative permittivity.

Wireless monitoring algorithm for wind turbine blades using Piezo‐electric energy harvesters

AbstractWind turbine blade failure can be catastrophic and lead to unexpected power interruptions. In this paper, a Structural Health Monitoring (SHM) algorithm is presented for wireless monitoring of wind turbine blades. The SHM algorithm utilizes accumulated strain energy data, such as would be acquired by piezoelectric materials. The SHM algorithm compares the accumulated strain energy at the same position on the three blades. This exploits the inherent triple redundancy of the blades and avoids the need for a structural model of the blade. The performance of the algorithm is evaluated using probabilistic metrics such as detection probability (True Positive) and false alarm rate (False Positive). The decision time is chosen to be sufficiently long that a particular damage level can be detected even in the presence of system sensor noise and wind variations. Finally, the proposed algorithm is evaluated with a case study of a utility‐scale turbine. The noise level is based on measurements acquired from strain sensors mounted on the blades of a Clipper Liberty C96 turbine. Strain energy changes associated with damage from matrix cracking and delamination are simulated with a finite element model. The case study demonstrates that the proposed algorithm can detect damage with a high probability based on a decision time period of approximately 50–200 days. Copyright © 2016 John Wiley & Sons, Ltd.

An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades

A laboratory study is reported regarding fatigue damage growth monitoring in a complete 45.7 m long wind turbine blade typically designed for a 2 MW generator. The main purpose of this study was to investigate the feasibility of in-service monitoring of the structural health of blades by acoustic emission (AE). Cyclic loading by compact resonant masses was performed to accurately simulate in-service load conditions and 187 kcs of fatigue were performed over periods which totalled 21 days, during which AE monitoring was performed with a 4 sensor array. Before the final 8 days of fatigue testing a simulated rectangular defect of dimensions 1 m × 0.05 m × 0.01 m was introduced into the blade material. The growth of fatigue damage from this source defect was successfully detected from AE monitoring. The AE signals were correlated with the growth of delamination up to 0.3 m in length and channel cracking in the final two days of fatigue testing. A high detection threshold of 40 dB was employed to suppress AE noise generated by the fatigue loading, which was a realistic simulation of the noise that would be generated in service from wind impact and acoustic coupling to the tower and nacelle. In order to decrease the probability of false alarm, a threshold of 45 dB was selected for further data processing. The crack propagation related AE signals discovered by counting only received pulse signals (bursts) from 4 sensors whose arrival times lay within the maximum variation of travel times from the damage source to the different sensors in the array. Analysis of the relative arrival times at the sensors by triangulation method successfully determined the location of damage growth, which was confirmed by photographic evidence. In view of the small scale of the damage growth relative to the blade size that was successfully detected, the developed AE monitoring methodology shows excellent promise as an in-service blade integrity monitoring technique capable of providing early warnings of developing damage before it becomes too expensive to repair.