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Dynamic Testing and Monitoring of Wind Turbines

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 24811

Special Issue Editors


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Guest Editor
Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal
Interests: operational modal analysis; dynamic tests and monitoring; structural health monitoring; fatigue assessment
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Co-Guest Editor
Acoustics and Vibrations Research Group (AVRG), Vrije Universiteit Brussel, B1050 Brussels, Belgium
Interests: Operational Modal Analysis, Structural Health Monitoring, Life Time Assesment, Offshore Structures, Sensing techologies

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Co-Guest Editor
Department of Mechanical Engineering, Vrije Universiteit Brussel, B-1050 Brussels, Belgium
Interests: Operational Modal Analysis, Dynamic Tests and Monitoring, Condition Monitoring, Rotating Machines, Big Data, IOT

Special Issue Information

Dear Colleagues,

Wind turbines, both onshore and offshore, have become one of the largest machines on Earth and consequently one of the most challenging structures for engineers. The large size of the blades and supporting structures makes then very flexible and therefore sensitive to the dynamic loads induced by both wind and waves. Furthermore, the evolution of offshore installations towards deeper waters, using larger bottom fixed foundations or floating platforms, even further increased the importance of good dynamic performance. Therefore, the dynamic testing and monitoring of their components and of the complete structure is crucial for design validation, condition assessment during operation, and fatigue analyses, with the aim of an estimation of the wind turbine components’ lifetime.

In this Special Issue, we would like to collect papers reflecting the current state-of-the-art in the dynamic testing of wind turbine components such as blades and drivetrains, and in the field dynamic testing and monitoring of these wind turbine components and their supporting structures, such as towers and onshore or offshore foundations and their connection.

Papers including a strong experimental component with in field validations are preferred, but works based on the illustration of new data processing strategies using numerical simulations or scaled laboratory models with high changes of application in full-scale structures may also be accepted. The description and validation of new sensing technologies applied to wind turbines components are very welcome.

We look forward to receiving your contribution!

Prof. Filipe Magalhães
Prof. Christof Devriendt
Prof. Jan Helsen
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Wind Turbine
  • Dynamic Testing
  • Dynamic Monitoring
  • Condition Assessment
  • Fatigue
  • Operational Modal Analysis
  • Blades
  • Drivetrain
  • Offshore Wind Turbines
  • Floating Wind Turbines

Published Papers (8 papers)

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Research

21 pages, 14021 KiB  
Article
Fatigue Stress Estimation for Submerged and Sub-Soil Welds of Offshore Wind Turbines on Monopiles Using Modal Expansion
by Maximilian Henkel, Wout Weijtjens and Christof Devriendt
Energies 2021, 14(22), 7576; https://doi.org/10.3390/en14227576 - 12 Nov 2021
Cited by 4 | Viewed by 1738
Abstract
The design of monopile foundations for offshore wind turbines is most often driven by fatigue. With the foundation price contributing to the total price of a turbine structure by more than 30%, wind farm operators seek to gain knowledge about the amount of [...] Read more.
The design of monopile foundations for offshore wind turbines is most often driven by fatigue. With the foundation price contributing to the total price of a turbine structure by more than 30%, wind farm operators seek to gain knowledge about the amount of consumed fatigue. Monitoring concepts are developed to uncover structural reserves coming from conservative designs in order to prolong the lifetime of a turbine. Amongst promising concepts is a wide array of methods using in-situ measurement data and extrapolating these results to desired locations below water surface and even seabed using models. The modal decomposition algorithm is used for this purpose. The algorithm obtains modal amplitudes from acceleration and strain measurements. In the subsequent expansion step these amplitudes are expanded to virtual measurements at arbitrary locations. The algorithm uses a reduced order model that can be obtained from either a FE model or measurements. In this work, operational modal analysis is applied to obtain the required stress and deflection shapes for optimal validation of the method. Furthermore, the measurements that are used as input for the algorithms are constrained to measurements from the dry part of the substructure. However, with subsoil measurement data available from a dedicated campaign, even validation for locations below mud-line is possible. After reconstructing strain history in arbitrary locations on the substructure, fatigue assessment over various environmental and operational conditions is carried out. The technique is found capable of estimating fatigue with high precision for locations above and below seabed. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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33 pages, 7997 KiB  
Article
Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades
by Gregory Duthé, Imad Abdallah, Sarah Barber and Eleni Chatzi
Energies 2021, 14(21), 7262; https://doi.org/10.3390/en14217262 - 03 Nov 2021
Cited by 11 | Viewed by 3382
Abstract
Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of [...] Read more.
Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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14 pages, 6033 KiB  
Article
Wind Turbine Tower Deformation Measurement Using Terrestrial Laser Scanning on a 3.4 MW Wind Turbine
by Paula Helming, Axel von Freyberg, Michael Sorg and Andreas Fischer
Energies 2021, 14(11), 3255; https://doi.org/10.3390/en14113255 - 02 Jun 2021
Cited by 13 | Viewed by 3049
Abstract
Wind turbine plants have grown in size in recent years, making an efficient structural health monitoring of all of their structures ever more important. Wind turbine towers deform elastically under the loads applied to them by wind and inertial forces acting on the [...] Read more.
Wind turbine plants have grown in size in recent years, making an efficient structural health monitoring of all of their structures ever more important. Wind turbine towers deform elastically under the loads applied to them by wind and inertial forces acting on the rotating rotor blades. In order to properly analyze these deformations, an earthbound system is desirable that can measure the tower’s movement in two directions from a large measurement working distance of over 150 m and a single location. To achieve this, a terrestrial laser scanner (TLS) in line-scanning mode with horizontal alignment was applied to measure the tower cross-section and to determine its axial (in the line-of-sight) and lateral (transverse to the line-of-sight) position with the help of a least-squares fit. As a result, the proposed measurement approach allowed for analyzing the tower’s deformation. The method was validated on a 3.4 MW wind turbine with a hub height of 128 m by comparing the measurement results to a reference video measurement, which recorded the nacelle movement from below and determined the nacelle movement with the help of point-tracking software. The measurements were compared in the time and frequency domain for different operating conditions, such as low/strong wind and start-up/braking of the turbine. There was a high correlation between the signals from the laser-based and the reference measurement in the time domain, and the same peak of the dominant tower oscillation was determined in the frequency domain. The proposed method was therefore an effective tool for the in-process structural health monitoring of tall wind turbine towers. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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19 pages, 20849 KiB  
Article
Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors
by João Pacheco, Gustavo Oliveira, Filipe Magalhães, Carlos Moutinho and Álvaro Cunha
Energies 2021, 14(2), 441; https://doi.org/10.3390/en14020441 - 15 Jan 2021
Cited by 7 | Viewed by 2988
Abstract
The reduction in operating and maintenance costs of wind farms is a fundamental element to guarantee the competitiveness and growth of the wind market. Wind turbines are highly dynamic structures prone to wear during their lifetime. Therefore, dynamic monitoring systems represent an excellent [...] Read more.
The reduction in operating and maintenance costs of wind farms is a fundamental element to guarantee the competitiveness and growth of the wind market. Wind turbines are highly dynamic structures prone to wear during their lifetime. Therefore, dynamic monitoring systems represent an excellent option to continuously evaluate their structural conditions. These systems allow early detection of damages, permit a proactive response, minimising downtime, and maximising productivity. In this context, the present paper describes the main results obtained with alternative instrumentation strategies tested in a 2.0 MW onshore wind turbine to reduce the costs of the monitoring equipment and at the same time ensure an adequate accuracy in structural condition evaluation. The data processing strategy encompasses the use of operational modal analysis combined with algorithms that deal with the particularities of operation of the wind turbines to continuously track the main vibration modes. After this automated online identification, the influence of the environmental and operating conditions on the tracked natural frequencies is mitigated, making the detection of abnormal variations of the natural frequencies possible, which might flag the appearance of damage. A database of continuously collected acceleration time series during one year is adopted to test the efficiency of alternative monitoring system layouts in detecting simulated damage scenarios. The tested alternative monitoring layouts present a varying number of sensors, alternative distributions in the wind turbine tower, and different sensor noise levels. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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18 pages, 1875 KiB  
Article
Dynamic Response Characterization of Floating Structures Based on Numerical Simulations
by Francisco Pimenta, Carlo Ruzzo, Giuseppe Failla, Felice Arena, Marco Alves and Filipe Magalhães
Energies 2020, 13(21), 5670; https://doi.org/10.3390/en13215670 - 29 Oct 2020
Cited by 7 | Viewed by 2064
Abstract
Output-only methods are widely used to characterize the dynamic behavior of very diverse structures. However, their application to floating structures may be limited due to their strong nonlinear behavior. Therefore, since there is very little experience on the application of these experimental tools [...] Read more.
Output-only methods are widely used to characterize the dynamic behavior of very diverse structures. However, their application to floating structures may be limited due to their strong nonlinear behavior. Therefore, since there is very little experience on the application of these experimental tools to these very peculiar structures, it is very important to develop studies, either based on numerical simulations or on real experimental data, to better understand their potential and limitations. In an initial phase, the use of numerical simulations permits a better control of all the involved variables. In this work, the Covariance-driven Stochastic Subspace Identification (SSI-COV) algorithm is applied to numerically simulated data of two different solutions to Floating Offshore Wind Turbines (FOWT) and for its capability of tracking the rigid body motion modal properties and susceptibility to different modeling restrictions and environmental conditions tested. The feasibility of applying the methods in an automated fashion in the processing of a large number of datasets is also evaluated. While the structure natural frequencies were consistently obtained from all the simulations, some difficulties were observed in the estimation of the mode shape components in the most changeling scenarios. The estimated modal damping coefficients were in good agreement with the expected results. From all the results, it can be concluded that output-only methods are capable of characterizing the dynamic behavior of a floating structure, even in the context of continuous dynamic monitoring using automated tracking of the modal properties, and should now be tested under uncontrolled environmental loads. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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17 pages, 9578 KiB  
Article
High-Resolution Structure-from-Motion for Quantitative Measurement of Leading-Edge Roughness
by Mikkel Schou Nielsen, Ivan Nikolov, Emil Krog Kruse, Jørgen Garnæs and Claus Brøndgaard Madsen
Energies 2020, 13(15), 3916; https://doi.org/10.3390/en13153916 - 31 Jul 2020
Cited by 7 | Viewed by 2717
Abstract
Over time, erosion of the leading edge of wind turbine blades increases the leading-edge roughness (LER). This may reduce the aerodynamic performance of the blade and hence the annual energy production of the wind turbine. As early detection is key for cost-effective maintenance, [...] Read more.
Over time, erosion of the leading edge of wind turbine blades increases the leading-edge roughness (LER). This may reduce the aerodynamic performance of the blade and hence the annual energy production of the wind turbine. As early detection is key for cost-effective maintenance, inspection methods are needed to quantify the LER of the blade. The aim of this proof-of-principle study is to determine whether high-resolution Structure-from-Motion (SfM) has the sufficient resolution and accuracy for quantitative inspection of LER. SfM provides 3D reconstruction of an object geometry using overlapping images of the object acquired with an RGB camera. Using information of the camera positions and orientations, absolute scale of the reconstruction can be achieved. Combined with a UAV platform, SfM has the potential for remote blade inspections with a reduced downtime. The tip of a decommissioned blade with an artificially enhanced erosion was used for the measurements. For validation, replica molding was used to transfer areas-of-interest to the lab for reference measurements using confocal microscopy. The SfM reconstruction resulted in a spatial resolution of 1 mm as well as a sub-mm accuracy in both the RMS surface roughness and the size of topographic features. In conclusion, high-resolution SfM demonstrated a successful quantitative reconstruction of LER. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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16 pages, 4892 KiB  
Article
An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes
by Mingzhu Tang, Qi Zhao, Steven X. Ding, Huawei Wu, Linlin Li, Wen Long and Bin Huang
Energies 2020, 13(4), 807; https://doi.org/10.3390/en13040807 - 12 Feb 2020
Cited by 59 | Viewed by 5887
Abstract
It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine [...] Read more.
It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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17 pages, 5236 KiB  
Article
Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background
by Lida Liao, Bin Huang, Qi Tan, Kan Huang, Mei Ma and Kang Zhang
Energies 2020, 13(4), 805; https://doi.org/10.3390/en13040805 - 12 Feb 2020
Cited by 6 | Viewed by 1856
Abstract
Given the prejudicial environmental effects of fossil-fuel based energy production, renewable energy sources can contribute significantly to the sustainability of human society. As a clean, cost effective and inexhaustible renewable energy source, wind energy harvesting has found a wide application to replace conventional [...] Read more.
Given the prejudicial environmental effects of fossil-fuel based energy production, renewable energy sources can contribute significantly to the sustainability of human society. As a clean, cost effective and inexhaustible renewable energy source, wind energy harvesting has found a wide application to replace conventional energy productions. However, concerns have been raised over the noise generated by turbine operating, which is helpful in fault diagnose but primarily identified for its adverse effects on the local ecosystems. Therefore, noise monitoring and separation is essential in wind turbine deployment. Recent developments in condition monitoring provide a solution for turbine noise and vibration analysis. However, the major component, aerodynamic noise is often distorted in modulation, which consequently affects the condition monitoring. This study is conducted to explore a novel approach to extract low-frequency elements from the aerodynamic noise background, and to improve the efficiency of online monitoring. A framework built on the spline envelope method and improved local mean decomposition has been developed for low-frequency noise extraction, and a case study with real near-field noises generated by a mountain-located wind turbine was employed to validate the proposed approach. Results indicate successful extractions with high resolution and efficiency. Findings of this research are also expected to further support the fault diagnosis and the improvement in condition monitoring of turbine systems. Full article
(This article belongs to the Special Issue Dynamic Testing and Monitoring of Wind Turbines)
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