IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4941-d416554.html
   My bibliography  Save this article

Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation

Author

Listed:
  • Miguel Á. Rodríguez-López

    (Instituto Complutense de Estudios Internacionales (ICEI), Complutense University of Madrid, 28040 Madrid, Spain)

  • Emilio Cerdá

    (Instituto Complutense de Estudios Internacionales (ICEI), Complutense University of Madrid, 28040 Madrid, Spain)

  • Pablo del Rio

    (Consejo Superior de Investigaciones Científicas (CSIC), Institute of Public Policies and Goods (IPP), C/Albasanz, 26-28, 28037 Madrid, Spain)

Abstract

Global warming represents a serious challenge, which requires the adoption of renewable energy technologies worldwide. However, it can negatively affect the availability of renewable energy resources, such as wind, which are needed for electricity generation. In this context, there is an increasing need for more accurate evaluations of wind turbine power curves. A novel methodology to model the power curves of wind turbines, which combines the use of artificial neural networks (ANN) and Fuzzy logic rules, is proposed in this paper. This methodology assesses the role of environmental temperature in the power curve and the impact of temperature increases on wind energy production. The application of this methodology is illustrated with the simulation of the impact of global warming on the electricity generation of a wind farm. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, it is shown that ANN combined with an expert system formed by a Fuzzy logic module fit power curve modeling processes well. The application of the methodology shows that an increase in temperatures would trigger a small reduction in the performance of wind turbines.

Suggested Citation

  • Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4941-:d:416554
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4941/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4941/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    2. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    3. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    4. Angel Gil & Miguel A. Sanz-Bobi & Miguel A. Rodríguez-López, 2018. "Behavior Anomaly Indicators Based on Reference Patterns—Application to the Gearbox and Electrical Generator of a Wind Turbine," Energies, MDPI, vol. 11(1), pages 1-15, January.
    5. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    6. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    7. Pryor, S.C. & Barthelmie, R.J., 2010. "Climate change impacts on wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 430-437, January.
    8. Nayeripour, Majid & Mahdi Mansouri, M., 2015. "An advanced analytical calculation and modeling of the electrical and mechanical harmonics behavior of Doubly Fed Induction Generator in wind turbine," Renewable Energy, Elsevier, vol. 81(C), pages 275-285.
    9. Lu, Lin & Yang, Hongxing & Burnett, John, 2002. "Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics," Renewable Energy, Elsevier, vol. 27(1), pages 1-12.
    10. Pelletier, Francis & Masson, Christian & Tahan, Antoine, 2016. "Wind turbine power curve modelling using artificial neural network," Renewable Energy, Elsevier, vol. 89(C), pages 207-214.
    11. Schreurs, Miranda A., 2012. "Breaking the impasse in the international climate negotiations: The potential of green technologies," Energy Policy, Elsevier, vol. 48(C), pages 5-12.
    12. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    13. Davy, Richard & Gnatiuk, Natalia & Pettersson, Lasse & Bobylev, Leonid, 2018. "Climate change impacts on wind energy potential in the European domain with a focus on the Black Sea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1652-1659.
    14. Helbing, Georg & Ritter, Matthias, 2020. "Improving wind turbine power curve monitoring with standardisation," Renewable Energy, Elsevier, vol. 145(C), pages 1040-1048.
    15. Fant, Charles & Adam Schlosser, C. & Strzepek, Kenneth, 2016. "The impact of climate change on wind and solar resources in southern Africa," Applied Energy, Elsevier, vol. 161(C), pages 556-564.
    16. Lydia, M. & Suresh Kumar, S. & Immanuel Selvakumar, A. & Edwin Prem Kumar, G., 2015. "Wind resource estimation using wind speed and power curve models," Renewable Energy, Elsevier, vol. 83(C), pages 425-434.
    17. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.
    18. Fan, Zhixin & Zhu, Caichao, 2019. "The optimization and the application for the wind turbine power-wind speed curve," Renewable Energy, Elsevier, vol. 140(C), pages 52-61.
    19. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    2. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
    3. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
    4. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    5. Mingzhe Zou & Sasa Z. Djokic, 2020. "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, MDPI, vol. 13(16), pages 1-30, August.
    6. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
    7. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    8. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
    9. Katopodis, Theodoros & Markantonis, Iason & Vlachogiannis, Diamando & Politi, Nadia & Sfetsos, Athanasios, 2021. "Assessing climate change impacts on wind characteristics in Greece through high resolution regional climate modelling," Renewable Energy, Elsevier, vol. 179(C), pages 427-444.
    10. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
    11. Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
    12. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
    13. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    14. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    15. Ravi Pandit & David Infield, 2018. "Gaussian Process Operational Curves for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 11(7), pages 1-20, June.
    16. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    17. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    18. Davide Astolfi & Francesco Castellani & Ludovico Terzi, 2018. "Wind Turbine Power Curve Upgrades," Energies, MDPI, vol. 11(5), pages 1-17, May.
    19. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
    20. Siyu Tao & Qingshan Xu & Andrés Feijóo & Stefanie Kuenzel & Neeraj Bokde, 2019. "Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient," Energies, MDPI, vol. 12(13), pages 1-14, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4941-:d:416554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.