Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature

Li, Y.; Coolen, F.P.A.; Zhu, C.; Tan, J.

Abstract

The hydraulic system is one of the most critical subsystems of wind turbines. It is used to reset the aerodynamic brakes. Because of this, the reliability of the hydraulic system is important to the functioning of the entire wind turbine. To realistically assess the reliability of the hydraulic system, we propose in this article the load-sharing based reliability model using survival signature to conduct system reliability assessment. In addition, due to the uncertainty of the failure rates, it is difficult to conduct accurate reliability analysis. The Markov-based fuzzy dynamic fault tree analysis method is developed to solve this issue for reliability modeling considering dynamic failure characteristics. Following this, we explore the reliability importance and the reliability sensitivity of redundant components. The relative importance of the components with respect to the system reliability is evaluated and ranked. Then the reliability sensitivity with respect to the distribution parameters of redundant components is studied. The results of the reliability sensitivity analysis investigate the effects of the distribution parameters on the entire system's reliability. The effectiveness and feasibility of the proposed methodology are demonstrated by the successful application on the hydraulic system of wind turbines.

Citation

Li, Y., Coolen, F., Zhu, C., & Tan, J. (2020). Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature. Renewable Energy, 153, 766-776. https://doi.org/10.1016/j.renene.2020.02.017

• Journal Article Type: Article
• Acceptance Date: Feb 5, 2020
• Online Publication Date: Feb 10, 2020
• Publication Date: Jun 30, 2020
• Deposit Date: Feb 6, 2020
• Publicly Available Date: Feb 10, 2021
• Journal: Renewable Energy
• Print ISSN: 0960-1481
• Electronic ISSN: 1879-0682
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 153
• Pages: 766-776
• DOI: https://doi.org/10.1016/j.renene.2020.02.017
• Public URL: https://durham-repository.worktribe.com/output/1272111