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Wind Turbine Data, Analysis and Models
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Wind Turbine Data, Analysis and Models

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 12618

Special Issue Editor

Prof. Dr. Daniel Villanueva Torres

E-Mail Website
Guest Editor
Departamento de Enxeñería Eléctrica, Universidade de Vigo, EEI, Campus de Lagoas-Marcosende, 36310 Vigo, Spain
Interests: electric energy storage; renewable energy; electric energy in buildings; electrical installations; electric vehicle; optimization of electric energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue is to give professional scholars and experts the opportunity to share their research related to wind turbines. It provides a medium for the presentation and discussion of the latest developments in the research, analysis, and modelling of wind turbines. The following topics are covered by this Special Issue:

  • Wind speed data analysis;
  • Wind speed data filtering;
  • Wind speed models or distributions;
  • Power curve models;
  • Wind power data analysis;
  • Wind power models or distributions;
  • Wind turbine models;
  • Simulation of wind turbines;
  • Influence of the weather in wind turbines;
  • Influence of the obstacles or terrain condition in wind turbines;
  • Wind turbine losses;
  • Ageing of wind turbines.

The papers accepted for publication in the journal must be original and refereed to a high standard. They may include new research findings, new applications of models, the analysis of real data and its interpretation, etc. All the manuscripts will be thoroughly refereed through a single-blind peer-review process.

Prof. Daniel Villanueva Torres
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 speed
  • wind power
  • power curve
  • wind energy
  • wind turbine

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Published Papers (4 papers)

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Research

18 pages, 2057 KiB  
Article
Wind Turbines Optimal Operation at Time Variable Wind Speeds
by Mihaela-Codruta Ancuti, Sorin Musuroi, Ciprian Sorandaru, Marian Dordescu and Geza Mihai Erdodi
Appl. Sci. 2020, 10(12), 4232; https://doi.org/10.3390/app10124232 - 20 Jun 2020
Cited by 10 | Viewed by 2566
Abstract
The wind turbine’s operation is affected by the wind speed variations, which cannot be followed by the wind turbine due to the large moment of the power plant’s inertia. The method proposed in this paper belongs to the wind turbine power curves (WTPC) [...] Read more.
The wind turbine’s operation is affected by the wind speed variations, which cannot be followed by the wind turbine due to the large moment of the power plant’s inertia. The method proposed in this paper belongs to the wind turbine power curves (WTPC) approach, which expresses the power curve of the permanent magnet synchronous generator (PMSG) by a set of mathematical equations. The WTPC research papers published before now have not taken into consideration the total power plant inertia at time-variable wind speeds, when the wind turbine’s optimal operation is very difficult to be reached, and its efficiency is thus threatened. The study is based on a wind turbine having a large moment of total inertia, and demonstrates, through extensive simulation results, that the optimal values of the PMSG’s power can be determined based on the kinetic motion equation. This PMSG’s optimal power represents an ideal time-varying curve, and the wind turbine should be controlled so as to closely follow it. For this purpose, proportional integral (PI) and proportional integral derivative (PID) type-based control methods were implemented and analyzed, so that the PMSG’s power oscillations could be reduced, and the PMSG’s angular speed value made comparable to the optimal one, meaning that the wind turbine operates within the optimal operation area, and is efficient. The simulations are actually the numerical solutions obtained by using the Scientific Workplace simulation environment, and they are based on the wind speed measurements collected from a wind farm located in Dobrogea, Romania. Full article
(This article belongs to the Special Issue Wind Turbine Data, Analysis and Models)
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11 pages, 2856 KiB  
Article
Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data
by Daniel Villanueva, Adrián Sixto, Andrés Feijóo, Antonio Fernández and Edelmiro Miguez
Appl. Sci. 2020, 10(9), 3317; https://doi.org/10.3390/app10093317 - 10 May 2020
Cited by 3 | Viewed by 2299
Abstract
Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of [...] Read more.
Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of data is available, a logistic function may be fitted and used as an accurate model for such curves, with the advantage that it describes the power curve by means of a very simple mathematical expression. Building such a curve from data can be achieved by different methods, such as using mean values or, alternatively, all the possible values for given intervals. However, when using the mean values, some information is missing and when using all the values the model obtained can be wrong. In this paper, some methods are proposed and applied to real data for comparison purposes. Among them, the one that combines data clustering and simulation is recommended in order to avoid some errors made by the other methods. Besides, a data filtering recommendation and two different assessment procedures for the error provided by the model are proposed. Full article
(This article belongs to the Special Issue Wind Turbine Data, Analysis and Models)
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17 pages, 1229 KiB  
Article
Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
by Sebastian Pfaffel, Stefan Faulstich and Kurt Rohrig
Appl. Sci. 2020, 10(3), 898; https://doi.org/10.3390/app10030898 - 30 Jan 2020
Cited by 3 | Viewed by 3899
Abstract
Key performance indicators (KPIs) are commonly used in the wind industry to support decision-making and to prioritize the work throughout a wind turbine portfolio. Still, there is little knowledge of the uncertainties of KPIs. This article intends to shed some light on the [...] Read more.
Key performance indicators (KPIs) are commonly used in the wind industry to support decision-making and to prioritize the work throughout a wind turbine portfolio. Still, there is little knowledge of the uncertainties of KPIs. This article intends to shed some light on the uncertainty and reliability of KPIs in general and performance KPIs in particular. For this purpose, different uncertainty causes are discussed, and three data handling related uncertainty causes are analyzed in detail for five KPIs. A local sensitivity analysis is followed by a more detailed analysis of the related uncertainties. The work bases on different sets of operational data, which are manipulated in a large number of experiments to carry out an empirical uncertainty analysis. The results show that changes in the data resolution, data availability, as well as missing inputs, can cause considerable uncertainties. These uncertainties can be reduced or even mitigated by simple measures in many cases. This article provides a comprehensive list of statements and recommendations to estimate the relevance of data handling related KPI uncertainties in the day-to-day work as well as approaches to correct KPIs for systematic deviations and simple steps to avoid pitfalls. Full article
(This article belongs to the Special Issue Wind Turbine Data, Analysis and Models)
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18 pages, 2141 KiB  
Article
Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
by Shenglei Pei and Yifen Li
Appl. Sci. 2019, 9(22), 4930; https://doi.org/10.3390/app9224930 - 16 Nov 2019
Cited by 26 | Viewed by 3096
Abstract
A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, [...] Read more.
A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers. Full article
(This article belongs to the Special Issue Wind Turbine Data, Analysis and Models)
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