Identification of critical components of wind turbines using FTA over time
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- Universidad de Castilla La-Mancha
Wind energy is currently the most widely implemented renewable energy source in global scale. Complex industrial multi-MW wind turbines are continuously being installed both onshore and offshore. Projects involving utility-scale wind turbines require optimisation of reliability, availability, maintainability and safety, in order to guarantee the financial viability of large scale wind energy projects, particularly offshore, in the forthcoming years. For this reason, critical wind turbine components must be identified and monitored as cost-effectively, reliably and efficiently as possible. The condition of industrial wind turbines can be qualitatively evaluated through the Fault Tree Analysis (FTA). The quantitative analysis requires high computational cost. In this paper, the Binary Decision Diagram (BDD) method is proposed for reducing this computational cost. In order to optimise the BDD a set of ranking methods of events has been considered; Level, Top-Down-Left-Right, AND, Depth First Search and Breadth-First Search. A quantitative analysis approach in order to find a general solution of a Fault Tree (FT) is presented. An illustrative case study of a FT of a wind turbine based on different research studies has been developed. Finally, this FT has been solved dynamically through the BDD approach in order to highlight the identification of the critical components of the wind turbine under different conditions, employing the following heuristic methods: Birnbaum, Criticality, Structural and Fussell-Vesely. The results provided by this methodology allow the performance of novel maintenance planning from a quantitative point of view.
|Early online date||14 Oct 2015|
|Publication status||Published - Mar 2016|
- Fault tree analysis, Binary diagram decisions, Wind turbines, Condition monitoring, Maintenance management