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Methods for Risk-Based Planning of O&M of Wind Turbines

Author

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  • Jannie Sønderkær Nielsen

    (Department of Civil Engineering, Aalborg University, Sofiendalsvej 11, 9200 Aalborg SV, Denmark)

  • John Dalsgaard Sørensen

    (Department of Civil Engineering, Aalborg University, Sofiendalsvej 11, 9200 Aalborg SV, Denmark)

Abstract

In order to make wind energy more competitive, the big expenses for operation and maintenance must be reduced. Consistent decisions that minimize the expected costs can be made based on risk-based methods. Such methods have been implemented for maintenance planning for oil and gas structures, but for offshore wind turbines, the conditions are different and the methods need to be adjusted accordingly. This paper gives an overview of various approaches to solve the decision problem: methods with decision rules based on observed variables, a method with decision rules based on the probability of failure, a method based on limited memory influence diagrams and a method based on the partially observable Markov decision process. The methods with decision rules based on observed variables are easy to use, but can only take the most recent observation into account, when a decision is made. The other methods can take more information into account, and especially, the method based on the Markov decision process is very flexible and accurate. A case study shows that the Markov decision process and decision rules based on the probability of failure are equally good and give lower costs compared to decision rules based on observed variables.

Suggested Citation

  • Jannie Sønderkær Nielsen & John Dalsgaard Sørensen, 2014. "Methods for Risk-Based Planning of O&M of Wind Turbines," Energies, MDPI, vol. 7(10), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:10:p:6645-6664:d:41274
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    References listed on IDEAS

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    Cited by:

    1. Lee, Dongkyu & Song, Junho, 2023. "Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Jannie S. Nielsen & John D. Sørensen, 2017. "Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades," Energies, MDPI, vol. 10(5), pages 1-13, May.
    3. Kroetz, H.M. & Moustapha, M. & Beck, A.T. & Sudret, B., 2020. "A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Bismut, Elizabeth & Straub, Daniel, 2021. "Optimal adaptive inspection and maintenance planning for deteriorating structural systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    6. 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.
    7. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    8. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    9. Simon Ambühl & John Dalsgaard Sørensen, 2017. "Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms," Energies, MDPI, vol. 10(4), pages 1-20, April.
    10. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Tautz-Weinert, Jannis & Yürüşen, Nurseda Y. & Melero, Julio J. & Watson, Simon J., 2019. "Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis," Renewable Energy, Elsevier, vol. 132(C), pages 93-105.

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