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Assessing offshore wind farm collision risks using AIS data: An overview

In: Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New Era. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 33

Author

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  • Weigell, Jürgen
  • Jahn, Carlos

Abstract

Purpose: Currently offshore wind farms are built in areas with high vessel traffic like the German Economic Exclusive Zone (EEZ). During the building phase and the operational phase a high amount of vessels will pass these offshore wind farms in close proximity and thus there is a risk of collision of a vessel with other vessels in the wind farms, e.g. installation vessels or service vessels or with objects like the wind turbines or the substation of an offshore wind farm. Methodology: In this paper relevant publications over the last ten years with a focus on the use of AIS (Automated Identification System) in regard to the collision risks of offshore wind farms will be investigated and sorted in a structured way. The publications will then be listed and classified into six sub groups. Findings: This analysis will show an overview of the current state of the art in using AIS data to determine the collision risks for offshore wind farms and the proposed methods to reduce these risks. Originality: The paper is original because there is currently no complete and up-to-date overview for the use of AIS-data to mitigate the collision risks.

Suggested Citation

  • Weigell, Jürgen & Jahn, Carlos, 2022. "Assessing offshore wind farm collision risks using AIS data: An overview," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 499-521, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:267197
    DOI: 10.15480/882.4716
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    References listed on IDEAS

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    1. Yu, Qing & Liu, Kezhong & Chang, Chia-Hsun & Yang, Zaili, 2020. "Realising advanced risk assessment of vessel traffic flows near offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    2. Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Scheidweiler, Tina & Jahn, Carlos, 2019. "Business analytics on AIS data: Potentials, limitations and perspectives," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 342-368, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Krzysztof Naus & Katarzyna Banaszak & Piotr Szymak, 2021. "The Methodology for Assessing the Impact of Offshore Wind Farms on Navigation, Based on the Automatic Identification System Historical Data," Energies, MDPI, vol. 14(20), pages 1-23, October.
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