Special Issue "Wind Turbine Monitoring through Operation Data Analysis"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Davide Astolfi
Website
Guest Editor
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Interests: wind turbines; condition monitoring; fault diagnosis; nonstationary machinery; control and monitoring; vibrations; applied statistics; numerical modeling; mechanical systems dynamics
Special Issues and Collections in MDPI journals
Prof. Dr. Francesco Castellani
Website
Guest Editor
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Interests: vibrations; aeroelasticity; mechanical systems dynamics; wind turbines; fault diagnosis; rotating machinery dynamics; control and monitoring; hydraulic and pneumatic power systems; aerodynamics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the widespread development of sensory, SCADA control, and data transmission systems has endowed wind energy scholars and practitioners with large amounts of information to be reduced through data mining into knowledge about the operation of wind turbine technology.

Operation data analysis has therefore become a keystone for wind turbine control and monitoring; nevertheless, due to the nonstationary conditions to which wind turbines are subjected, innovative statistical and computational methods are required for producing reliable results with practical impact on wind farm operation and management and, eventually, on the cost of energy.

Several methods have been developed in recent years for wind turbine monitoring: power curve or, in general, operational curve analysis and modeling; multivariate regressions for the data-driven modeling of wind turbine power, taking into account its joint dependence on ambient conditions and working parameters; and subcomponent temperature analysis and modeling for fault diagnosis. Furthermore, the analysis of operation data has demonstrated promising potential for the detection of sensory faults and control system biases (for example, the systematic zero-point shift of the yaw angle).

On these grounds, it can be stated that the literature about operation data analysis for wind turbine monitoring is productive and stimulating; therefore, the present Special Issue aims to collect innovative research contributions, possibly supported by real-world test case analysis.

Topics of interest include, but are not limited to:

  • Wind turbine power curve analysis;
  • Data mining methods for wind turbine yaw and/or pitch behavior analysis;
  • Statistical, artificial intelligence, and deep learning data analysis methods for wind turbine performance monitoring;
  • Wind turbine fault diagnosis through operation data analysis;
  • Operation assessment of wind turbine optimization technology;
  • Validation of CFD simulations, wake models, and engineering models against real-world operation data;
  • Time-resolved operation data analysis.

Dr. Davide Astolfi
Prof. Francesco Castellani
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 papers will be 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 1800 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 turbines
  • operation data analysis
  • performance control
  • condition monitoring
  • fault diagnosis

Published Papers (1 paper)

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Research

Open AccessArticle
Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact
Energies 2020, 13(9), 2351; https://doi.org/10.3390/en13092351 - 08 May 2020
Cited by 2
Abstract
The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact [...] Read more.
The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4 affects the performance up to the order of the 1.5% of the Annual Energy Production. Full article
(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)
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