Design optimisation of wind turbine towers with reliability-based calibration of partial safety factors

Al-Sanad, Shaikha; Parol, Jafarali; Wang, Lin; Kolios, Athanasios

Abstract

Having an optimal design of the wind turbine tower, with a minimum mass (cost) while fulfilling multiple design constraints, plays an important role in ensuring an economic and safe design of the wind turbine. During the design of wind turbine towers, partial safety factors (PSFs) are currently commonly used to account for the uncertainties in the loads and material properties due to its easy implementation. The values of PSFs given in design standard are generic and are not derived for a specific design. For a site-specific design of wind turbine towers, the details of the load parameters, such as the type of distributions and the coefficient of variation, can be obtained through the condition monitoring system. With these information, the PSFs can be calibrated based on the reliability method, meeting the target reliability index and avoiding over or under engineering of wind turbine tower structures. In this work, a parametric finite element analysis model is integrated with a genetic algorithm to develop a structural optimisation model of wind turbine towers. The optimisation framework minimises the tower mass under multiple design constraints. The optimisation model has been applied to a representative 2.0 MW onshore wind turbine tower. PSFs are calibrated on the basis of reliability. The optimal tower design with calibrated PSFs is compared against the design with un-calibrated PSFs. Results indicate that the tower design with calibrated PSFs achieves a mass reduction of 2.9% in comparison to the design with un-calibrated PSFs.

Citation

Al-Sanad, S., Parol, J., Wang, L., & Kolios, A. (2023). Design optimisation of wind turbine towers with reliability-based calibration of partial safety factors. Energy Reports, 9, 2548-2556. https://doi.org/10.1016/j.egyr.2023.01.090

• Journal Article Type: Article
• Acceptance Date: Jan 19, 2023
• Online Publication Date: Jan 30, 2023
• Publication Date: 2023-12
• Deposit Date: Nov 30, 2023
• Publicly Available Date: Nov 30, 2023
• Journal: Energy Reports
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Pages: 2548-2556
• DOI: https://doi.org/10.1016/j.egyr.2023.01.090
• Public URL: https://nottingham-repository.worktribe.com/output/17071578