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Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ

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  • Zhang, Chen
  • Yang, Tao

Abstract

The complex structure and harsh working environment of wind turbines cause frequent failures and unavailability of these turbines in wind farms. To promote the long-term stable development of wind power and enhance its market competitiveness, the reduction of operation and maintenance costs is particularly important, which are estimated to account for approximately 1/3 of the total life cycle cost. With the continuous increase in the size and number of wind turbines, wind farm maintenance tasks and resources are increasing and becoming unpredictable. The realization of the dynamic scheduling of maintenance tasks and resources under various constraints has become vital. In this study, an optimal multi-objective model of maintenance planning and resource allocation for wind farms is established. The maintenance tasks are obtained according to the preset maintenance strategy and current operating status of the wind turbine components. The dynamic requirements of maintenance planning and resource allocation for different wind farms in adjacent areas are periodically generated, and the Non-dominated sorting genetic algorithm-ΙΙ (NSGA-ΙΙ) is adopted to conduct a combinatorial optimization process. The validity of the proposed model are verified by a corresponding case study, along with a comparative analysis with other optimization algorithms and a sensitivity study of different parameters.

Suggested Citation

  • Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:1540-1549
    DOI: 10.1016/j.renene.2020.10.125
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    References listed on IDEAS

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    1. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    2. Manjunath Patel G C & Prasad Krishna & Mahesh B. Parappagoudar & Pandu Ranga Vundavilli, 2016. "Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 7(1), pages 55-74, January.
    3. B. Y. Qu & Q. Zhou & J. M. Xiao & J. J. Liang & P. N. Suganthan, 2017. "Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-14, February.
    4. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    5. Aurélien Froger & Michel Gendreau & Jorge E. Mendoza & Eric Pinson & Louis-Martin Rousseau, 2018. "Solving a wind turbine maintenance scheduling problem," Journal of Scheduling, Springer, vol. 21(1), pages 53-76, February.
    6. Erguido, A. & Crespo Márquez, A. & Castellano, E. & Gómez Fernández, J.F., 2017. "A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms," Renewable Energy, Elsevier, vol. 114(PB), pages 843-856.
    7. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
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    Cited by:

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    2. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    4. Andrés Cacereño & David Greiner & Blas J. Galván, 2021. "Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II," Mathematics, MDPI, vol. 9(15), pages 1-39, July.

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