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Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets

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  • Saleh, Ali
  • Chiachío, Manuel
  • Salas, Juan Fernández
  • Kolios, Athanasios

Abstract

With the emerging monitoring technologies, condition-based maintenance is nowadays a reality for the wind energy industry. This is important to avoid unnecessary maintenance actions, which increase the operation and maintenance costs, along with the costs associated with downtime. However, condition-based maintenance requires a policy to transform system conditions into decision-making while considering monetary restrictions and energy productivity objectives. To address this challenge, an intelligent Petri net algorithm has been created and applied to model and optimize offshore wind turbines’ operation and maintenance. The proposed method combines advanced Petri net modelling with Reinforcement Learning and is formulated in a general manner so it can be applied to optimize any Petri net model. The resulting methodology is applied to a case study considering the operation and maintenance of a wind turbine using operation and degradation data. The results show that the proposed method is capable to reach optimal condition-based maintenance policy considering maximum availability (equal to 99.4%) and minimal operational costs.

Suggested Citation

  • Saleh, Ali & Chiachío, Manuel & Salas, Juan Fernández & Kolios, Athanasios, 2023. "Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006287
    DOI: 10.1016/j.ress.2022.109013
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    as
    1. Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Yeter, B. & Garbatov, Y. & Guedes Soares, C., 2020. "Risk-based maintenance planning of offshore wind turbine farms," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Ghamlouch, Houda & Fouladirad, Mitra & Grall, Antoine, 2019. "The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 614-623.
    4. Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Yeter, B. & Garbatov, Y. & Guedes Soares, C., 2022. "Life-extension classification of offshore wind assets using unsupervised machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    7. 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.
    8. Chiachío, Manuel & Saleh, Ali & Naybour, Susannah & Chiachío, Juan & Andrews, John, 2022. "Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. M’hammed Sahnoun & David Baudry & Navonil Mustafee & Anne Louis & Philip Andi Smart & Phil Godsiff & Belahcene Mazari, 2019. "Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2981-2997, December.
    10. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Abdollahzadeh, Hadi & Atashgar, Karim & Abbasi, Morteza, 2016. "Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups," Renewable Energy, Elsevier, vol. 88(C), pages 247-261.
    12. Wang, Jingjing & Miao, Yonghao, 2021. "Optimal preventive maintenance policy of the balanced system under the semi-Markov model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Yang, Ao & Qiu, Qingan & Zhu, Mingren & Cui, Lirong & Chen, Weilin & Chen, Jianhui, 2022. "Condition-based maintenance strategy for redundant systems with arbitrary structures using improved reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    14. Sarker, Bhaba R. & Faiz, Tasnim Ibn, 2016. "Minimizing maintenance cost for offshore wind turbines following multi-level opportunistic preventive strategy," Renewable Energy, Elsevier, vol. 85(C), pages 104-113.
    15. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    16. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    17. Houda Ghamlouch & Mitra Fouladirad & Antoine Grall, 2019. "The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties," Post-Print hal-02365402, HAL.
    18. Koukoura, Sofia & Scheu, Matti Niclas & Kolios, Athanasios, 2021. "Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    19. Yürüşen, Nurseda Y. & Rowley, Paul N. & Watson, Simon J. & Melero, Julio J., 2020. "Automated wind turbine maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    20. Andrews, John & Fecarotti, Claudia, 2017. "System design and maintenance modelling for safety in extended life operation," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 95-108.
    21. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    22. Stock-Williams, Clym & Swamy, Siddharth Krishna, 2019. "Automated daily maintenance planning for offshore wind farms," Renewable Energy, Elsevier, vol. 133(C), pages 1393-1403.
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    Cited by:

    1. Hongyan Dui & Yulu Zhang & Yun-An Zhang, 2023. "Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
    2. Niemi, Arto & Skobiej, Bartosz & Kulev, Nikolai & Sill Torres, Frank, 2024. "Modeling offshore wind farm disturbances and maintenance service responses within the scope of resilience," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Zhu, Dongping & Huang, Xiaogang & Ding, Zhixia & Zhang, Wei, 2024. "Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Chahrour, Nour & Bérenguer, Christophe & Tacnet, Jean-Marc, 2024. "Incorporating cascading effects analysis in the maintenance policy assessment of torrent check dams against torrential floods," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. Li, Yan & Zhang, Wei & Liu, Baoliang & Wang, Xiaofeng, 2024. "Availability and maintenance strategy under time-varying environments for redundant repairable systems with PH distributions," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    8. Deng, Wanyi & Ma, Xiaoxue & Qiao, Weiliang, 2024. "A novel methodology to quantify the impact of safety barriers on maritime operational risk based on a probabilistic network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Kristjanpoller, Fredy & Cárdenas-Pantoja, Nicolás & Viveros, Pablo & Pascual, Rodrigo, 2023. "Wind farm life cycle cost modelling based on oversizing capacity under load sharing configuration," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    10. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Lopez, Javier Contreras & Kolios, Athanasios & Wang, Lin & Chiachio, Manuel & Dimitrov, Nikolay, 2024. "Reliability-based leading edge erosion maintenance strategy selection framework," Applied Energy, Elsevier, vol. 358(C).
    12. Gan, Chenyu & Ding, Shuiting & Qiu, Tian & Liu, Peng & Ma, Qinglin, 2024. "Model-based safety analysis with time resolution (MBSA-TR) method for complex aerothermal–mechanical systems of aero-engines," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    13. Dui, Hongyan & Zhang, Yulu & Bai, Guanghan, 2024. "Analysis of variable system cost and maintenance strategy in life cycle considering different failure modes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    14. Lopez, Javier Contreras & Kolios, Athanasios, 2024. "An autonomous decision-making agent for offshore wind turbine blades under leading edge erosion," Renewable Energy, Elsevier, vol. 227(C).

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