Special Issue "Lifetime Extension of Wind Turbines and Wind Farms"

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

Deadline for manuscript submissions: 20 January 2021.

Special Issue Editors

Prof. Dr. Emilio Gomez-Lazaro
Website
Guest Editor
1. Renewable Energy Research Institute, Universidad de Castilla-La Mancha, 13001 Ciudad Real, Spain;
2. Department of Electrical Engineering, Electronics, Control Communications, Escuela Técnica Superior de Ingenieros Industriales de Albacete, 02071 Albacete, Spain
Interests: power electronics and power systems; renewable energy systems; modeling; dynamic performance of inverter-based generation in power systems; maintenance of renewable energy power installations; transmission and distribution studies
Special Issues and Collections in MDPI journals
Dr. Estefania Artigao
Website
Guest Editor
1. Renewable Energy Research Institute, Universidad de Castilla-La Mancha, Spain.
2. Department of Electrical Engineering, Electronics, Control Communications. Escuela Técnica Superior de Ingenieros Industriales de Albacete, Spain.
Interests: operations and maintenance of wind turbines; condition monitoring of wind turbines; current signature analysis; doubly-fed induction generators; reliability and availability of wind farms; onshore and offshore wind farms; failure rates and downtime of wind turbines
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues:

Currently, wind energy is the most mature renewable energy. With the current global environmental concern, it will continue to grow, as it is expected to play an important role in the future electricity market.

Wind turbines have experienced a remarkable growth in a relatively short period of time, thus still needing to address important challenges. Regarding end-of-life scenarios of wind turbines and wind farms, decisions are complex, and the experiences to date are very limited. Aging wind farms face three options: lifetime extension, repowering or decommissioning. Thus, in order to reach a solution that increases the operator’s revenue, is legally feasible, and does not compromise safety of operators or citizens, end-of-life scenarios must be carefully reviewed to make a decision.

The present Special Issue aims at investigating current trends, identifying existing challenges and awarding the latest research in lifetime extension of wind turbines, both for onshore and offshore wind farms.

Topics of interest include but are not limited to:

  • End-of-life issues of wind turbines
  • End-of-life issues of wind farms
  • Operations and maintenance.
  • New operational strategies
  • Wind turbine assessment
  • Novel condition monitoring techniques
  • Novel health structural assessment
  • Safety and risks associated to lifetime extension
  • Influence of reliability and availability on lifetime extension
  • Analysis of the age of the wind turbine fleet in different locations, regions, countries, and/or geographical areas
  • Economic analyses (CAPEX, OPEX, COE, etc.) towards decision making
  • Application of new techniques, including Artificial Intelligence, Machine Learning or Big Data
  • Related review papers

Prof. Dr. Emilio Gomez-Lazaro
Dr. Estefania Artigao
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

  • lifetime extension
  • remaining useful life
  • reliability and availability
  • operations and maintenance
  • operational strategies
  • condition monitoring
  • asset management
  • safety and risks
  • decision making
  • OPEX

Published Papers (3 papers)

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Research

Open AccessArticle
A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis
Energies 2020, 13(8), 2086; https://doi.org/10.3390/en13082086 - 21 Apr 2020
Cited by 3
Abstract
Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. [...] Read more.
Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature. Full article
(This article belongs to the Special Issue Lifetime Extension of Wind Turbines and Wind Farms)
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Open AccessArticle
Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements
Energies 2020, 13(6), 1474; https://doi.org/10.3390/en13061474 - 20 Mar 2020
Cited by 2
Abstract
Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of [...] Read more.
Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring. Full article
(This article belongs to the Special Issue Lifetime Extension of Wind Turbines and Wind Farms)
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Open AccessArticle
T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case
Energies 2020, 13(6), 1306; https://doi.org/10.3390/en13061306 - 11 Mar 2020
Abstract
Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but [...] Read more.
Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model. Full article
(This article belongs to the Special Issue Lifetime Extension of Wind Turbines and Wind Farms)
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