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Power optimization of wind turbines with data mining and evolutionary computation

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  • Kusiak, Andrew
  • Zheng, Haiyang
  • Song, Zhe

Abstract

A data-driven approach for maximization of the power produced by wind turbines is presented. The power optimization objective is accomplished by computing optimal control settings of wind turbines using data mining and evolutionary strategy algorithms. Data mining algorithms identify a functional mapping between the power output and controllable and non-controllable variables of a wind turbine. An evolutionary strategy algorithm is applied to determine control settings maximizing the power output of a turbine based on the identified model. Computational studies have demonstrated meaningful opportunities to improve the turbine power output by optimizing blade pitch and yaw angle. It is shown that the pitch angle is an important variable in maximizing energy captured from the wind. Power output can be increased by optimization of the pitch angle. The concepts proposed in this paper are illustrated with industrial wind farm data.

Suggested Citation

  • Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2010. "Power optimization of wind turbines with data mining and evolutionary computation," Renewable Energy, Elsevier, vol. 35(3), pages 695-702.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:3:p:695-702
    DOI: 10.1016/j.renene.2009.08.018
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    References listed on IDEAS

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    1. Yurdusev, M.A. & Ata, R. & Çetin, N.S., 2006. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks," Energy, Elsevier, vol. 31(12), pages 2153-2161.
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    1. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    2. Rocha, P.A. Costa & Carneiro de Araujo, J.W. & Lima, R.J. Pontes & Vieira da Silva, M.E. & Albiero, D. & de Andrade, C.F. & Carneiro, F.O.M., 2018. "The effects of blade pitch angle on the performance of small-scale wind turbine in urban environments," Energy, Elsevier, vol. 148(C), pages 169-178.
    3. 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.
    4. Liu, Zhengliang & Bhattacharjee, Kalyan Shankar & Tian, Fang-Bao & Young, John & Ray, Tapabrata & Lai, Joseph C.S., 2019. "Kinematic optimization of a flapping foil power generator using a multi-fidelity evolutionary algorithm," Renewable Energy, Elsevier, vol. 132(C), pages 543-557.
    5. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
    6. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    7. Konstantinos Konstas & Panos T. Chountalas & Eleni A. Didaskalou & Dimitrios A. Georgakellos, 2023. "A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study," Energies, MDPI, vol. 16(17), pages 1-26, August.
    8. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    9. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    10. Kusiak, Andrew & Zhang, Zijun, 2012. "Control of wind turbine power and vibration with a data-driven approach," Renewable Energy, Elsevier, vol. 43(C), pages 73-82.
    11. La Cava, William & Danai, Kourosh & Spector, Lee & Fleming, Paul & Wright, Alan & Lackner, Matthew, 2016. "Automatic identification of wind turbine models using evolutionary multiobjective optimization," Renewable Energy, Elsevier, vol. 87(P2), pages 892-902.
    12. Yan Pei & Zheng Qian & Bo Jing & Dahai Kang & Lizhong Zhang, 2018. "Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection," Energies, MDPI, vol. 11(3), pages 1-14, March.
    13. Vera-Tudela, Luis & Kühn, Martin, 2017. "Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions," Renewable Energy, Elsevier, vol. 107(C), pages 352-360.
    14. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    15. Pousinho, H.M.I. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "A risk-averse optimization model for trading wind energy in a market environment under uncertainty," Energy, Elsevier, vol. 36(8), pages 4935-4942.

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