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Structural Testing and Health Monitoring of Wind Turbines
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Structural Testing and Health 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: 31 December 2024 | Viewed by 2593

Special Issue Editors

Dr. Sérgio Pereira

E-Mail Website1 Website2
Guest Editor
Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: structural health monitoring; operational modal analysis; wind turbines; structural dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Filipe Magalhães

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 them 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, has 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 estimating the wind turbine components’ lifetime.

In this Special Issue, we aim 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 contributions!

Dr. Sérgio Pereira
Dr. Filipe Magalhães
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

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Published Papers (2 papers)

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Research

27 pages, 2633 KiB  
Article
Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach
by Waqar Ali, Idriss El-Thalji, Knut Erik Teigen Giljarhus and Andreas Delimitis
Energies 2024, 17(23), 5856; https://doi.org/10.3390/en17235856 - 22 Nov 2024
Viewed by 403
Abstract
Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion [...] Read more.
Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time- and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time- and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events. Full article
(This article belongs to the Special Issue Structural Testing and Health Monitoring of Wind Turbines)
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13 pages, 2043 KiB  
Article
Wind Turbine Geometrical and Operation Variables Reconstruction from Blade Acceleration Measurements
by Francisco Pimenta, Carlos Moutinho and Filipe Magalhães
Energies 2024, 17(1), 229; https://doi.org/10.3390/en17010229 - 31 Dec 2023
Cited by 1 | Viewed by 1190
Abstract
To develop reliable numerical models and better interpret monitoring campaigns experimental data of wind turbines, knowing the structure operation conditions, in particular the rotor angular velocity and blades’ pitch angle, is of paramount importance, but often not known due to confidentiality restrictions, or [...] Read more.
To develop reliable numerical models and better interpret monitoring campaigns experimental data of wind turbines, knowing the structure operation conditions, in particular the rotor angular velocity and blades’ pitch angle, is of paramount importance, but often not known due to confidentiality restrictions, or known with low time resolution (typically 10 min average values). In this work, it is shown analytically that blades accelerations measurements contain valuable information that allow for a better characterisation of the effective rotor shaft tilt and blades cone angle for different operating conditions. It is also shown that these measurements can be used to reconstruct the time history of the rotor angular velocity and blades’ pitch angle. After presented in an analytical framework, the methodology is validated with experimental data of two full scale wind turbines. The successful reconstruction of the rotor operating conditions shows that the method presented can be used to provide further insight into the dynamics of the structure that aids monitoring data analysis and provides an alternative method to monitor the SCADA systems themselves. The paper combines quite unique experimental data collected at two operating rotors with original data processing strategies that provide very valuable information to researchers and wind turbine operators. Full article
(This article belongs to the Special Issue Structural Testing and Health Monitoring of Wind Turbines)
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