Data-Driven Infrastructure Planning for Offshore Wind Farms

Saxena, Isha; Kazemtabrizi, Behzad; Troffaes, Matthias C.M.; Crabtree J., Christopher

Abstract

Offshore wind farms are one of the major renewable energy resources that can help the UK to reach its net zero target. Under the 10 point plan of the green revolution, the UK is set to quadruple its wind energy production by increasing its offshore wind capacity to 40GW by 2030. Research needs to be conducted to study the failure and repair processes of wind turbines under various conditions as the current models make a simplifying assumption that the failure/repair rate remains constant over time. This research aims to create a more accurate model using SCADA data. In this research, different mathematical models are fitted to the time to failure and time to repair data of wind turbine components using frequentist methods (such as Maximum Likelihood Estimation) and Bayesian methods. Further analysis will be conducted using complex system analysis considering the failures of each electrical and mechanical component of the wind turbine. The aim of this project is to perform a more accurate reliability analysis that can help to further drive down costs of wind energy by potentially reducing the downtimes of the wind turbines.

Citation

Saxena, I., Kazemtabrizi, B., Troffaes, M. C., & Crabtree J., C. (2024, May). Data-Driven Infrastructure Planning for Offshore Wind Farms. Presented at Torque 2024, Florence, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Torque 2024
• Start Date: May 29, 2024
• End Date: May 31, 2024
• Acceptance Date: Mar 1, 2024
• Online Publication Date: Jun 1, 2024
• Publication Date: Jun 1, 2024
• Deposit Date: Apr 13, 2024
• Publicly Available Date: Jun 1, 2024
• Journal: Journal of Physics: Conference Series
• Print ISSN: 1742-6588
• Electronic ISSN: 1742-6596
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 2767
• Issue: 6
• Article Number: 062002
• Series Title: Journal of Physics: Conference Series (JCPS)
• Series ISSN: 1742-6596
• DOI: https://doi.org/10.1088/1742-6596/2767/6/062002
• Public URL: https://durham-repository.worktribe.com/output/2383929
• Publisher URL: https://iopscience.iop.org/journal/1742-6596