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Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods

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: 25 July 2024 | Viewed by 11913

Special Issue Editor

Dr. Davide Astolfi

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: wind turbines; condition monitoring; fault diagnosis; non-stationary machinery; control and monitoring; vibrations; applied statistics; numerical modelling; mechanical systems dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Greater exploitation of renewable energy is, at present, at the centre of the global policy agenda. In this context, wind turbines represent an extremely promising technology. On the one hand, new installations with large rotors are growing at a remarkable rate, necessitating precise evaluation of their actual capacity. On the other hand, a vast fraction of the wind turbines operating in Europe are presently reaching the end of their expected lifetime, and thus, judicious decisions will need to be taken regarding their repowering, decommissioning and so on.

While wind turbine and wind farm performance research is increasingly relevant, this objective poses several scientific and technological challenges. Wind turbines are complex machines subjected to nonstationary operation conditions, and in real-world plants it is impractical to monitor all the environmental conditions on which the extracted power depends.

Considering this premise, this Special Issue will present high-quality contributions covering all aspects of wind farms and wind turbine performance. While contributions on the following topics are particularly welcome, the list should not be considered exclusive:

  • Wind turbine power curves;
  • SCADA data analysis;
  • Diagnosis of wind turbine under-performance and faults;
  • Wind turbine and wind farm wakes and turbulence;
  • Wind farm blockage;
  • Jets and wind turbine performance;
  • Wind power forecast;
  • Wind turbine life cycle assessment;
  • Wind turbine ageing and end-of-life issues;
  • Wind turbine technology;
  • Wind tunnel testing;
  • LiDAR and anemometry;
  • Wind farm control and wake steering;
  • Yaw and pitch control;
  • Wind turbines in complex terrain;
  • Computational fluid dynamics
  • Large wind turbines;
  • Offshore wind farms;
  • Floating wind turbines;
  • Microwind turbines.

Dr. Davide Astolfi
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. 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.

Published Papers (7 papers)

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Editorial

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4 pages, 168 KiB  
Editorial
Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers
by Davide Astolfi
Energies 2023, 16(9), 3614; https://0-doi-org.brum.beds.ac.uk/10.3390/en16093614 - 22 Apr 2023
Cited by 2 | Viewed by 1039
Abstract
Wind energy is going to be the leading renewable source of the next decades [...] Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)
5 pages, 197 KiB  
Editorial
Individuation of Wind Turbine Systematic Yaw Error through SCADA Data
by Davide Astolfi, Ravi Pandit, Linyue Gao and Jiarong Hong
Energies 2022, 15(21), 8165; https://0-doi-org.brum.beds.ac.uk/10.3390/en15218165 - 01 Nov 2022
Cited by 5 | Viewed by 1458
Abstract
Much attention in the wind energy literature is devoted to condition monitoring [...] Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)
4 pages, 179 KiB  
Editorial
Wind Turbine Performance Decline with Age
by Davide Astolfi and Ravi Pandit
Energies 2022, 15(14), 5225; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145225 - 19 Jul 2022
Cited by 2 | Viewed by 1953
Abstract
Wind turbines, as any technical system, are expected to have an efficiency that declines in time [...] Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)

Research

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17 pages, 9594 KiB  
Article
Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control
by Mou Lin and Fernando Porté-Agel
Energies 2023, 16(6), 2542; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062542 - 08 Mar 2023
Cited by 3 | Viewed by 1655
Abstract
This study investigated the power production and blade fatigue of a three-turbine array subjected to active yaw control (AYC) in full-wake and partial-wake configurations. A framework of a two-way coupled large eddy simulation (LES) and an aeroelastic blade simulation was applied to simulate [...] Read more.
This study investigated the power production and blade fatigue of a three-turbine array subjected to active yaw control (AYC) in full-wake and partial-wake configurations. A framework of a two-way coupled large eddy simulation (LES) and an aeroelastic blade simulation was applied to simulate the atmospheric boundary layer (ABL) flow through the turbines and the structural responses of the blades. The mean power outputs and blade fatigue loads were extracted from the simulation results. By exploring the feasible AYC decision space, we found that in the full-wake configuration, the local power-optimal AYC strategy with positive yaw angles endures less flapwise blade fatigue and more edgewise blade fatigue than the global power-optimal strategy. In the partial-wake configuration, applying positive AYC in certain inflow wind directions achieves higher optimal power gains than that in the full-wake scenario and reduces blade fatigue from the non-yawed benchmark. Using the blade element momentum (BEM) theory, we reveal that the aforementioned differences in flapwise blade fatigue are due to the differences in the azimuthal distributions of the local relative velocity on blade sections, resulting from the vertical wind shear and blade rotation. Furthermore, the difference in the blade force between the positively and negatively yawed front-row turbine induces different wake velocities and turbulence distributions, causing different fatigue loads on the downwind turbine exposed to the wake. Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)
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14 pages, 3951 KiB  
Article
Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models
by Ju-Yeol Ryu, Bora Lee, Sungho Park, Seonghyeon Hwang, Hyemin Park, Changhyeong Lee and Dohyeon Kwon
Energies 2022, 15(24), 9403; https://0-doi-org.brum.beds.ac.uk/10.3390/en15249403 - 12 Dec 2022
Cited by 5 | Viewed by 1259
Abstract
The rising share of renewable energy in the energy mix brings with it new challenges such as power curtailment and lack of reliable large-scale energy grid. The forecasting of wind power generation for provision of flexibility, defined as the ability to absorb and [...] Read more.
The rising share of renewable energy in the energy mix brings with it new challenges such as power curtailment and lack of reliable large-scale energy grid. The forecasting of wind power generation for provision of flexibility, defined as the ability to absorb and manage fluctuations in the demand and supply by storing energy at times of surplus and releasing it when needed, is important. In this study, short-term forecasting models of wind power generation were developed using the conventional time-series method and hybrid models using support vector regression (SVR) based on rolling origin recalibration. For the application of the methodology, the meteorological database from Korea Meteorological Administration and actual operating data of a wind power turbine (2.3 MW) from 1 January to 31 December 2015 were used. The results showed that the proposed SVR model has higher forecasting accuracy than the existing time-series methods. In addition, the conventional time-series model has high accuracy under proper curation of wind turbine operation data. Therefore, the analysis results reveal that data curation and weather information are as important as the model for wind power forecasting. Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)
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18 pages, 5224 KiB  
Article
Research on Anomaly Detection of Wind Farm SCADA Wind Speed Data
by Wu Wen, Yubao Liu, Rongfu Sun and Yuewei Liu
Energies 2022, 15(16), 5869; https://0-doi-org.brum.beds.ac.uk/10.3390/en15165869 - 12 Aug 2022
Cited by 7 | Viewed by 1868
Abstract
Supervisory control and data acquisition (SCADA) systems are critical for wind power grid integration and wind farm operation and maintenance. However, wind turbines are affected by regulation, severe weather factors, and mechanical failures, resulting in abnormal SCADA data that seriously affect the usage [...] Read more.
Supervisory control and data acquisition (SCADA) systems are critical for wind power grid integration and wind farm operation and maintenance. However, wind turbines are affected by regulation, severe weather factors, and mechanical failures, resulting in abnormal SCADA data that seriously affect the usage of SCADA systems. Thus, strict and effective data quality control of the SCADA data are crucial. The traditional anomaly detection methods based on either “power curve” or statistical evaluation cannot comprehensively detect abnormal data. In this study, a multi-approach based abnormal data detection method for SCADA wind speed data quality control is developed. It is mainly composed of the EEMD (Ensemble Empirical Mode Decomposition)-BiLSTM network model, wind speed correlation between adjacent wind turbines, and the deviation detection model based on dynamic power curve fitting. The proposed abnormal data detection method is tested on SCADA data from a real wind farm, and statistical analysis of the results verifies that this method can effectively detect abnormal SCADA wind data. The proposed method can be readily applied for real-time operation to support an effective use of SCADA data for wind turbine control and wind power prediction. Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)
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20 pages, 30235 KiB  
Article
Experimental Investigation of the Cooperation of Wind Turbines
by Piotr Wiklak, Michal Kulak, Michal Lipian and Damian Obidowski
Energies 2022, 15(11), 3906; https://0-doi-org.brum.beds.ac.uk/10.3390/en15113906 - 25 May 2022
Cited by 1 | Viewed by 1402
Abstract
The article discusses the wind tunnel experimental investigation of two turbines (the downstream unit placed fully in the wake of the upstream one) at various turbulence intensity levels and wind turbine separation distances, at a Reynolds number of approximately 105. The [...] Read more.
The article discusses the wind tunnel experimental investigation of two turbines (the downstream unit placed fully in the wake of the upstream one) at various turbulence intensity levels and wind turbine separation distances, at a Reynolds number of approximately 105. The velocity deficit due to the upstream turbine operation is reduced as the wake mixes with the undisturbed flow, which may be enhanced by increasing the turbulence intensity. In a natural environment, this may be provoked by natural wind gusts or changes in the wind inflow conditions. Increased levels of turbulence intensity enlarge the plateau of optimum wind turbine operation—this results in the turbine performance being less prone to variations of tip speed ratio. Another important set of results quantifies the influence of the upstream turbine operation at non-optimal tip speed ratio on the overall system performance, as the downstream machine gains more energy from the wake flow. Thus, all power output maximisation analyses of wind turbine layout in a cluster should encompass not only the locations and distances between the units, but also their operating parameters (TSR, but also pitch or yaw control of the upstream turbine(s)). Full article
(This article belongs to the Special Issue Wind Turbines and Wind Farms Performance Analysis through Numerical and Experimental Methods)
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