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A framework for data integration of offshore wind farms

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  • Nguyen, Trinh Hoang
  • Prinz, Andreas
  • Friisø, Trond
  • Nossum, Rolf
  • Tyapin, Ilya

Abstract

Operation and maintenance play an important role in maximizing the yield and minimizing the downtime of wind turbines, especially offshore wind farms where access can be difficult due to harsh weather conditions for long periods. It contributes up to 25–30% to the cost of energy generation. Improved operation and maintenance (O&M) practices are likely to reduce the cost of wind energy and increase safety. In order to optimize the O&M, the importance of data exchange and knowledge sharing within the offshore wind industry must be realized. With more data available, it is possible to make better decisions, and thereby improve the recovery rates and reduce the operational costs. This article describes the development of a framework for data integration to optimize the remote operations of offshore wind farms.

Suggested Citation

  • Nguyen, Trinh Hoang & Prinz, Andreas & Friisø, Trond & Nossum, Rolf & Tyapin, Ilya, 2013. "A framework for data integration of offshore wind farms," Renewable Energy, Elsevier, vol. 60(C), pages 150-161.
    • Handle: RePEc:eee:renene:v:60:y:2013:i:c:p:150-161
    DOI: 10.1016/j.renene.2013.05.002
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    References listed on IDEAS

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    1. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
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    3. Hameed, Z. & Vatn, J. & Heggset, J., 2011. "Challenges in the reliability and maintainability data collection for offshore wind turbines," Renewable Energy, Elsevier, vol. 36(8), pages 2154-2165.
    4. Esteban, M. Dolores & Diez, J. Javier & López, Jose S. & Negro, Vicente, 2011. "Why offshore wind energy?," Renewable Energy, Elsevier, vol. 36(2), pages 444-450.
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    Cited by:

    1. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.
    2. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.
    3. Alizadeh, S.M. & Ozansoy, C., 2016. "The role of communications and standardization in wind power applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 944-958.
    4. 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).
    5. 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.
    6. Ellabban, Omar & Abu-Rub, Haitham & Blaabjerg, Frede, 2014. "Renewable energy resources: Current status, future prospects and their enabling technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 748-764.
    7. Hadžić, Neven & Kozmar, Hrvoje & Tomić, Marko, 2014. "Offshore renewable energy in the Adriatic Sea with respect to the Croatian 2020 energy strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 597-607.
    8. Sarker, Bhaba R. & Faiz, Tasnim Ibn, 2017. "Minimizing transportation and installation costs for turbines in offshore wind farms," Renewable Energy, Elsevier, vol. 101(C), pages 667-679.
    9. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.

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