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Special Issue "Wind Turbine Technologies"

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A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Turbomachinery".

Deadline for manuscript submissions: closed (12 June 2023) | Viewed by 8424

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Special Issue Editors

Dr. Davide Astolfi

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Guest Editor

Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Interests: wind power; wind turbines; renewable power generation; condition monitoring; fault diagnosis; control and monitoring; electrical systems; data analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Antonio J. Marques Cardoso

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Guest Editor

CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
Interests: diagnosis and fault tolerance of electrical machines, power electronics and drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, stronger exploitation of renewable energy is at the centre of the policy agenda worldwide, and, in this context, wind turbines are the most promising technology.

Nevertheless, wind turbines are complex machines, subjected to non-stationary operation conditions; therefore, it is fundamental to promote deeper scientific comprehension of the operation of this technology. Furthermore, despite horizontal-axis wind turbines substantially constituting a mature technology, new challenges have arisen with the increasing size of the machines and with the exploitation in complex environments.

Based on these considerations, the objective of this Special Issue is to collect contributions devoted to the general topic of wind turbine technologies. Therefore, the Special Issue has a wide scope, and, in accordance to the scientific purposes of the journal, the common ground is expected to be a focus on the machine. Theoretical and experimental contributions are both welcome.

Contributions that deal with the following aspects, applied to wind turbine technologies, are particularly welcome (although the list should not be considered as exclusive):

Structural Health Monitoring;
Condition Monitoring;
Fault Diagnosis and Prognosis;
Fatigue and Life Cycle Assessment;
Rotor Design;
Wind Turbine and Wind Farm Control;
Wind Turbine Blades: Design and Materials;
Wind Turbine Generators;
Converters and Power Electronics;
Wind Turbines Pitch;
Wind Turbines Yaw;
Performance Analysis;
Wind Tunnel Testing;
Vibration and Noise Reduction;
Horizontal- and Vertical-Axis Micro Wind Turbines;
SCADA Data Analysis and Wind Turbine Sensors;
Measurement Applications;
Wind Turbine Wakes;
Floating Wind Turbines;
Wind Turbines in Complex Terrain;
Computational Fluid Dynamics and Aeroelastic Simulations.

Dr. Davide Astolfi
Prof. Dr. Antonio J. Marques Cardoso
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. Machines is an international peer-reviewed open access monthly 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.

Published Papers (5 papers)

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Research

Open AccessArticle

Wind/Wave Testing of a 1:70-Scale Performance-Matched Model of the IEA Wind 15 MW Reference Wind Turbine with Real-Time ROSCO Control and Floating Feedback

and

Machines 2023, 11(9), 865; https://doi.org/10.3390/machines11090865 - 28 Aug 2023

Viewed by 726

Abstract

Experimental results from the Floating Offshore-wind and Controls Advanced Laboratory (FOCAL) experimental program, which tested a performance-matched model of the IEA Wind 15 MW Reference Turbine on a 1:70 scale floating semisubmersible platform, are compared with OpenFAST simulations. Four experimental campaigns were performed, and data from the fourth campaign, which focused on wind and wave testing of the scaled floating wind turbine system, are considered. Simulations of wave-only, wind-only, and wind/wave environments are performed in OpenFAST, and results for key metrics are compared with the experiment. Performance of the real-time Reference OpenSource COntroller (ROSCO) in above-rated wind conditions, including the effects of the floating feedback loop, are investigated. Results show good agreement in mean values for key metrics, and hydrodynamic effects are matched well. Differences in the surge resonant behavior of the platform are identified and discussed. The effect of the controller and floating feedback loop is evident in both the experiment and OpenFAST, showing significant reduction in platform pitch response and tower base bending load near the platform pitch natural frequency. Full article

(This article belongs to the Special Issue Wind Turbine Technologies)

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Open AccessArticle

Working Condition Identification Method of Wind Turbine Drivetrain

and

Machines 2023, 11(4), 495; https://doi.org/10.3390/machines11040495 - 20 Apr 2023

Viewed by 629

Abstract

The operation state of the wind turbine drivetrain is complex and variable, making it difficult to accurately evaluate under the drivetrain’s anomalies. In order to accurately identify the operating state of the main drivetrain, a method for working condition identification is proposed. Firstly, appropriate working condition identification parameters are selected and distinguished from the working condition feature parameters. Secondly, the aerodynamic power prediction model is established, which solves the problem of inaccurate theoretical estimation. Finally, after the historical working conditions are classified, the working condition identification model is established, and the proposed method is analyzed and validated by cases. The results show that the method can accurately identify the working conditions, avoiding the influence of an abnormal state of drivetrain, and provide a basis for real-time state monitoring and evaluation. Full article

(This article belongs to the Special Issue Wind Turbine Technologies)

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Open AccessEditor’s ChoiceArticle

Early Fault Warning Method of Wind Turbine Main Transmission System Based on SCADA and CMS Data

and

Machines 2022, 10(11), 1018; https://doi.org/10.3390/machines10111018 - 02 Nov 2022

Viewed by 1150

Abstract

The main transmission system of wind turbines is a multi-component coupling system, and its operational state is complex and varied. These lead to frequent false alarms and missed alarms in existing monitoring systems. To accurately obtain the operational state of the main transmission system and detect its abnormal operation, an early fault warning method for the main transmission system based on SCADA and CMS data is proposed. Firstly, the SCADA and CMS feature parameters relevant to the operating status of the main transmission system are selected by two different methods separately, and the correlation mechanism between the feature parameters and the operating characteristics of the main transmission system is further analyzed. Secondly, the Long Short-Term Memory (LSTM) network-based prediction model of the main transmission system operating parameters is established, in which SCADA and CMS feature parameters are fused as the input feature vectors. Then, the predicted residuals of the state evaluation parameters are used as the operational state evaluation index. The early fault warning model is established by Analytic Hierarchy Process (AHP) and Kernel Density Estimation (KDE). Finally, a case study is used to verify the correct performance of the proposed method. The results show that this method can realize early warning functions 73 h earlier than the existing SCADA system. The method can provide a theoretical basis for the safe operation and condition-based maintenance of wind turbines. Full article

(This article belongs to the Special Issue Wind Turbine Technologies)

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Open AccessArticle

Experimental Study on the Influence of a Two-Dimensional Cosine Hill on Wind Turbine Wake

and

Machines 2022, 10(9), 753; https://doi.org/10.3390/machines10090753 - 01 Sep 2022

Viewed by 1026

Abstract

The accurate prediction of wind energy distribution on the terrain has been of great significance for wind farm selection. Therefore, this paper evaluated the influence of a two-dimensional hill with a relatively large slope (i.e., 0.83) on wind turbine wake. Firstly, the wakes on flat terrain and wind characteristics around a single hill were investigated by using a hot-wire anemometer. Subsequently, wake distributions combined with the hill were measured when the turbine was located 2D in front and on the hilltop. The results of the hill showed that a flow separation was formed within 6D of the leeward side (where D is the rotor diameter). The turbulence intensity increased initially and then decreased as height increased, leading to a high turbulence region 2.28 times the hill height, according to the experiment of a wind turbine combined with the hill. In conclusion, wake recovery was promoted on the windward side and 4D behind the hilltop. As the longitudinal coordinate increased, the intensity of the turbulence changed to a single peak, and the peak value was more than twice as high as on flat terrain. Based on this, it may be possible to optimize the design of wind turbines for better performance. Full article

(This article belongs to the Special Issue Wind Turbine Technologies)

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Open AccessArticle

Multi-Objective Optimization and Optimal Airfoil Blade Selection for a Small Horizontal-Axis Wind Turbine (HAWT) for Application in Regions with Various Wind Potential

Vahid Akbari

Mohammad Naghashzadegan

Ramin Kouhikamali

Farhad Afsharpanah

and

Wahiba Yaïci

Machines 2022, 10(8), 687; https://doi.org/10.3390/machines10080687 - 13 Aug 2022

Cited by 16 | Viewed by 3896

Abstract

The type of airfoil with small wind turbine blades should be selected based on the wind potential of the area in which the turbine is used. In this study, 10 low Reynolds number airfoils, namely, BW-3, E387, FX 63-137, S822, S834, SD7062, SG6040, SG6043, SG6051, and USNPS4, were selected and their performance was evaluated in a 1 kW wind turbine in terms of the power coefficient and also the startup time, by performing a multi-objective optimization study. The blade element momentum technique was utilized to perform the calculations of the power coefficient and startup time and the differential evolution algorithm was employed to carry out the optimization. The results reveal that the type of airfoil used in the turbine blade, aside from the aerodynamic performance, completely affects the turbine startup performance. The SG6043 airfoil has the highest power coefficient and the BW-3 airfoil presents the shortest startup time. The high lift-to-drag ratio of the SG6043 airfoil and the low inertia of the turbine blades fitted with the BW-3 airfoil make them suitable for operation in windy regions and areas with low wind speeds, respectively. Full article

(This article belongs to the Special Issue Wind Turbine Technologies)

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