IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v202y2020ics036054422030846x.html
   My bibliography  Save this article

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

Author

Listed:
  • Yin, Xiuxing
  • Zhao, Xiaowei
  • Lin, Jin
  • Karcanias, Aris

Abstract

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.

Suggested Citation

  • Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s036054422030846x
    DOI: 10.1016/j.energy.2020.117739
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422030846X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117739?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    2. Salazar, Jean C. & Weber, Philippe & Nejjari, Fatiha & Sarrate, Ramon & Theilliol, Didier, 2017. "System reliability aware Model Predictive Control framework," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 663-672.
    3. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    4. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    5. Kalyanmoy Deb & Nikhil Padhye, 2014. "Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms," Computational Optimization and Applications, Springer, vol. 57(3), pages 761-794, April.
    6. Vianna Neto, Júlio Xavier & Guerra Junior, Elci José & Moreno, Sinvaldo Rodrigues & Hultmann Ayala, Helon Vicente & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2018. "Wind turbine blade geometry design based on multi-objective optimization using metaheuristics," Energy, Elsevier, vol. 162(C), pages 645-658.
    7. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    8. Zhixin, Wang & Chuanwen, Jiang & Qian, Ai & Chengmin, Wang, 2009. "The key technology of offshore wind farm and its new development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 216-222, January.
    9. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    10. Nikhil Padhye & Piyush Bhardawaj & Kalyanmoy Deb, 2013. "Improving differential evolution through a unified approach," Journal of Global Optimization, Springer, vol. 55(4), pages 771-799, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
    2. Chen, Xi & Yu, Ruyi & Ullah, Sajid & Wu, Dianming & Li, Zhiqiang & Li, Qingli & Qi, Honggang & Liu, Jihui & Liu, Min & Zhang, Yundong, 2022. "A novel loss function of deep learning in wind speed forecasting," Energy, Elsevier, vol. 238(PB).

    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. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    2. Wang, Feng & Chen, Jincheng & Xu, Bing & Stelson, Kim A., 2019. "Improving the reliability and energy production of large wind turbine with a digital hydrostatic drivetrain," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Yin, Xiuxing & Zhang, Wencan & Jiang, Zhansi & Pan, Li, 2020. "Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization," Renewable Energy, Elsevier, vol. 160(C), pages 974-986.
    4. Tanvir Ahmad & Abdul Basit & Muneeb Ahsan & Olivier Coupiac & Nicolas Girard & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
    5. Adedipe, Oyewole & Brennan, Feargal & Kolios, Athanasios, 2016. "Review of corrosion fatigue in offshore structures: Present status and challenges in the offshore wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 141-154.
    6. Leijon, Mats & Skoglund, Annika & Waters, Rafael & Rehn, Alf & Lindahl, Marcus, 2010. "On the physics of power, energy and economics of renewable electric energy sources – Part I," Renewable Energy, Elsevier, vol. 35(8), pages 1729-1734.
    7. Wu, Chunying & Sun, Lingfang & Piao, Heng & Yao, Lijia, 2024. "Adaptive fuzzy finite time integral sliding mode control of the coordinated system for 350 MW supercritical once-through boiler unit to enhance flexibility," Energy, Elsevier, vol. 302(C).
    8. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    9. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
    10. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    11. Cao, Yankai & Zavala, Victor M. & D’Amato, Fernando, 2018. "Using stochastic programming and statistical extrapolation to mitigate long-term extreme loads in wind turbines," Applied Energy, Elsevier, vol. 230(C), pages 1230-1241.
    12. Wang, Xuefei & Zeng, Xiangwu & Li, Xinyao & Li, Jiale, 2019. "Investigation on offshore wind turbine with an innovative hybrid monopile foundation: An experimental based study," Renewable Energy, Elsevier, vol. 132(C), pages 129-141.
    13. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    15. Zhangling Xiao & Mingjin Zhang & Zhongmin Liang & Jian Wang & Yude Zhu & Binquan Li & Yiming Hu & Jun Wang & Xiaolei Jiang, 2024. "Improved Multi-objective Butterfly Optimization Algorithm and its Application in Cascade Reservoirs Optimal Operation Considering Ecological Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4803-4821, September.
    16. Mohamed R. Torkomany & Hassan Shokry Hassan & Amin Shoukry & Mohamed Hussein & Chihiro Yoshimura & Mohamed Elkholy, 2023. "Investigation of Optimum Sustainable Designs for Water Distribution Systems from Multiple Economic, Operational, and Health Perspectives," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    17. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    18. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    19. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    20. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.

    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:eee:energy:v:202:y:2020:i:c:s036054422030846x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.