An integrated operational system to reduce O&M cost of offshore wind farms

Kai Wang; Xiaoli Jiang; Rudy Negenborn; X. Yan; Y. Yuan

An integrated operational system to reduce O&M cost of offshore wind farms

Kai Wang, Xiaoli Jiang, Rudy Negenborn, X. Yan, Y. Yuan

Transport Engineering and Logistics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Offshore wind is a relatively new industry and it is generally more expensive to generate electricity than many alternative renewable sources. Operation & Maintenance (O&M) makes up a significant part of the overall cost of running Offshore Wind Turbines (OWT). Since the O&M associated responsibility is shared among turbine manufacturers, wind farm operators and the offshore transmission owners, this has inevitably led to lack of information, duplication of effort and less efficiency. Big data analytics is one great technique that will drive future growth. In this paper, an integrated operational system of offshore wind farm is proposed deploying big data analytics. Firstly, the current state of the O&M of offshore wind farm and the big data analytics are introduced. Afterwards, a predictive maintenance model and a maintenance implementation model are proposed, and an integrated operational system is developed incorporating those two models in order to optimize maintenance planning and implementation. Finally, the possible contribution of such a system to a more effective O&M of offshore wind farm is discussed.

Original language	English
Title of host publication	Progress in Maritime Technology and Engineering
Subtitle of host publication	Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018)
Editors	Carlos Guedes Soares, T.A. Santos
Place of Publication	Leiden, The Netherlands
Publisher	CRC Press / Balkema - Taylor & Francis Group
Chapter	54
Pages	469-474
ISBN (Print)	978-1-138-58539-3
Publication status	Published - 2018
Event	MARTECH 2018: 4th International Conference on Maritime Technology and Engineering - Lisbon, Portugal Duration: 7 May 2018 → 9 May 2018

Conference

Conference	MARTECH 2018: 4th International Conference on Maritime Technology and Engineering
Country/Territory	Portugal
City	Lisbon
Period	7/05/18 → 9/05/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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Wang, K., Jiang, X., Negenborn, R., Yan, X., & Yuan, Y. (2018). An integrated operational system to reduce O&M cost of offshore wind farms. In C. Guedes Soares, & T. A. Santos (Eds.), Progress in Maritime Technology and Engineering: Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018) (pp. 469-474). CRC Press / Balkema - Taylor & Francis Group.

Wang, Kai ; Jiang, Xiaoli ; Negenborn, Rudy et al. / An integrated operational system to reduce O&M cost of offshore wind farms. Progress in Maritime Technology and Engineering: Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018). editor / Carlos Guedes Soares ; T.A. Santos. Leiden, The Netherlands : CRC Press / Balkema - Taylor & Francis Group, 2018. pp. 469-474

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title = "An integrated operational system to reduce O&M cost of offshore wind farms",

abstract = "Offshore wind is a relatively new industry and it is generally more expensive to generate electricity than many alternative renewable sources. Operation & Maintenance (O&M) makes up a significant part of the overall cost of running Offshore Wind Turbines (OWT). Since the O&M associated responsibility is shared among turbine manufacturers, wind farm operators and the offshore transmission owners, this has inevitably led to lack of information, duplication of effort and less efficiency. Big data analytics is one great technique that will drive future growth. In this paper, an integrated operational system of offshore wind farm is proposed deploying big data analytics. Firstly, the current state of the O&M of offshore wind farm and the big data analytics are introduced. Afterwards, a predictive maintenance model and a maintenance implementation model are proposed, and an integrated operational system is developed incorporating those two models in order to optimize maintenance planning and implementation. Finally, the possible contribution of such a system to a more effective O&M of offshore wind farm is discussed. ",

author = "Kai Wang and Xiaoli Jiang and Rudy Negenborn and X. Yan and Y. Yuan",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; MARTECH 2018: 4th International Conference on Maritime Technology and Engineering ; Conference date: 07-05-2018 Through 09-05-2018",

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Wang, K, Jiang, X , Negenborn, R, Yan, X & Yuan, Y 2018, An integrated operational system to reduce O&M cost of offshore wind farms. in C Guedes Soares & TA Santos (eds), Progress in Maritime Technology and Engineering: Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018). CRC Press / Balkema - Taylor & Francis Group, Leiden, The Netherlands, pp. 469-474, MARTECH 2018: 4th International Conference on Maritime Technology and Engineering, Lisbon, Portugal, 7/05/18.

An integrated operational system to reduce O&M cost of offshore wind farms. / Wang, Kai; Jiang, Xiaoli ; Negenborn, Rudy et al.
Progress in Maritime Technology and Engineering: Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018). ed. / Carlos Guedes Soares; T.A. Santos. Leiden, The Netherlands: CRC Press / Balkema - Taylor & Francis Group, 2018. p. 469-474.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Wang, Kai

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AU - Yan, X.

AU - Yuan, Y.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2018

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N2 - Offshore wind is a relatively new industry and it is generally more expensive to generate electricity than many alternative renewable sources. Operation & Maintenance (O&M) makes up a significant part of the overall cost of running Offshore Wind Turbines (OWT). Since the O&M associated responsibility is shared among turbine manufacturers, wind farm operators and the offshore transmission owners, this has inevitably led to lack of information, duplication of effort and less efficiency. Big data analytics is one great technique that will drive future growth. In this paper, an integrated operational system of offshore wind farm is proposed deploying big data analytics. Firstly, the current state of the O&M of offshore wind farm and the big data analytics are introduced. Afterwards, a predictive maintenance model and a maintenance implementation model are proposed, and an integrated operational system is developed incorporating those two models in order to optimize maintenance planning and implementation. Finally, the possible contribution of such a system to a more effective O&M of offshore wind farm is discussed.

AB - Offshore wind is a relatively new industry and it is generally more expensive to generate electricity than many alternative renewable sources. Operation & Maintenance (O&M) makes up a significant part of the overall cost of running Offshore Wind Turbines (OWT). Since the O&M associated responsibility is shared among turbine manufacturers, wind farm operators and the offshore transmission owners, this has inevitably led to lack of information, duplication of effort and less efficiency. Big data analytics is one great technique that will drive future growth. In this paper, an integrated operational system of offshore wind farm is proposed deploying big data analytics. Firstly, the current state of the O&M of offshore wind farm and the big data analytics are introduced. Afterwards, a predictive maintenance model and a maintenance implementation model are proposed, and an integrated operational system is developed incorporating those two models in order to optimize maintenance planning and implementation. Finally, the possible contribution of such a system to a more effective O&M of offshore wind farm is discussed.

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Wang K, Jiang X , Negenborn R, Yan X, Yuan Y. An integrated operational system to reduce O&M cost of offshore wind farms. In Guedes Soares C, Santos TA, editors, Progress in Maritime Technology and Engineering: Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018). Leiden, The Netherlands: CRC Press / Balkema - Taylor & Francis Group. 2018. p. 469-474

An integrated operational system to reduce O&M cost of offshore wind farms

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