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Applied Sciences
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Advances and Challenges in Wind Turbine Mechanics
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Advances and Challenges in Wind Turbine Mechanics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1062

Special Issue Editors

Dr. Ryszard Szwaba

E-Mail Website
Guest Editor
Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-231 Gdańsk, Poland
Interests: experimental aerodynamics; flow control; wind turbines; heat transfer
Prof. Dr. Małgorzata A. Śmiałek

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Ship Technology, Institute of Naval Architecture and Ocean Engineering, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Interests: biophysics; photoelectron spectroscopy; physical chemistry; biochemistry
Dr. Piotr Pawłowski

E-Mail Website
Guest Editor
Institute of Fundamental Technological Research Polish Academy of Sciences (IPPT PAN), Pawinskiego 5B, 02-106 Warsaw, Poland
Interests: dynamic analysis designing structural dynamics structural analysis finite element analysis; finite element modeling; nonlinear analysis; FE analysis; stress analysis; solid mechanics
Dr. Janusz Telega

E-Mail Website
Assistant Guest Editor
Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-231 Gdańsk, Poland
Interests: wind tunnel experiments; shock waves; wind turbines; signal processing; machine learning; M2M

Special Issue Information

Dear Colleagues,

Renewable energy sources have been playing a significant role in global electricity production for some time now, with wind turbine technology being one of the most intensively developed branches. However, this field poses significant challenges as it involves solving problems across distinct disciplines, such as mechanics and electrotechnics, to convert the kinetic energy of wind into usable electric energy.

The mechanical aspect involves the actual turbine and the necessary surrounding structure of the nacelle and tower, which must be optimized for aerodynamics and material properties such as tensile strength. The electrotechnical aspect includes the energoelectronic and control-security systems.

All of these factors must be analyzed to maximize efficiency and security for both small single kilowatt units and large offshore megawatt units.

The specifics of wind turbines pose various challenges both in field and model tests. Due to the long operational lifespan of wind turbines, on-site testing typically spans a year to ensure accurate wind conditions. The model turbine must be placed in a remote location, away from urban areas, and the height of the turbine causes further difficulties in access during the testing process. These challenges become more severe for offshore turbines.

Another challenge is obtaining and analyzing accurate measurement data. Once results are available, a time-series analysis must be conducted to gain insights into both the averaged characteristics of the turbine and its behavior in extreme weather conditions.

If wind tunnel tests are conducted, scaling flow parameters and projecting results to real-sized objects must be carefully considered.

To address these challenges, and the many others we may encounter in the field, we introduce the Special Issue entitled "Advances and Challenges in Wind Turbine Mechanics".

The topics of interest include, but are not limited to, the following:

  • Innovative wind turbine design shapes;
  • Wind tunnel and field testing of turbines;
  • Numerical simulations;
  • Statistical approaches and risk assessments;
  • Turbine selection for specific wind conditions;
  • Assessment of spot energy potential;
  • Design specifications for different scale turbines, including large-scale, medium-scale, small-scale, and micro-scale;
  • Optimization techniques in design;
  • Performance assessment using algorithmic techniques such as machine learning and neural networks;
  • Remote site monitoring and automated evaluation, including hardware and software (ICT, automated report generation, etc.);
  • Performance improvement of wind turbines.

Dr. Ryszard Szwaba
Prof. Dr. Małgorzata A. Śmiałek
Dr. Piotr Pawłowski
Guest Editors

Dr. Janusz Telega
Assistant Guest Editor

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. Applied Sciences 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 2400 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 design
  • wind turbine experimental research
  • wind turbine simulations
  • wind turbine optimization
  • wind potential assessment
  • wind turbine data processing

Published Papers (1 paper)

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Research

13 pages, 2464 KiB  
Article
Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
by Yu Zhang, Runcai Huang and Zhiwei Li
Appl. Sci. 2023, 13(16), 9276; https://doi.org/10.3390/app13169276 - 15 Aug 2023
Cited by 2 | Viewed by 772
Abstract
Aiming at the problem that existing wind turbine gearbox fault prediction models often find it difficult to distinguish the importance of different data frames and are easily interfered with by non-important and irrelevant signals, thus causing a reduction in fault diagnosis accuracy, a [...] Read more.
Aiming at the problem that existing wind turbine gearbox fault prediction models often find it difficult to distinguish the importance of different data frames and are easily interfered with by non-important and irrelevant signals, thus causing a reduction in fault diagnosis accuracy, a wind turbine gearbox fault prediction model based on the attention-weighted long short-term memory network (AW-LSTM) is proposed. Specifically, the gearbox vibration signal is decomposed by empirical modal decomposition (EMD), to contain seven different frequency components and one residual component. The decomposed signal is passed through a four-layer LSTM network, to extract the fault features. The attention mechanism is introduced, to reweight the hidden states, in order to strengthen the attention to the important features. The proposed method captures the intrinsic long-term temporal correlation of timing gearbox signals through a long short-term memory network, and resorts to recursive attentional weighting, to efficiently distinguish the contribution of different frames and to exclude the influence of irrelevant or interfering data on the model. The results show that the proposed AW-LSTM wind turbine gearbox fault prediction model has an inference time of 36 s on two publicly available wind turbine fault detection datasets, with a root mean square error of 1.384, an average absolute error of 0.983, and an average absolute percentage error of 9.638, and that the AW-LSTM prediction model is able to efficiently extract the characteristics of wind turbine gearbox faults, with a shorter inference time and better fault prediction. Full article
(This article belongs to the Special Issue Advances and Challenges in Wind Turbine Mechanics)
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