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

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  • 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
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    References listed on IDEAS

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    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.
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