Dynamic Reliability Based Design Optimization of Offshore Wind Turbines Considering Uncertainties
- Hezhen Yang (University of Glasgow) | Xiao Zhang | Fei Xiao (Nanyang Technological University)
- Document ID
- International Society of Offshore and Polar Engineers
- The Thirteenth ISOPE Pacific/Asia Offshore Mechanics Symposium, 14-17 October, Jeju, Korea
- Publication Date
- Document Type
- Conference Paper
- 2018. International Society of Offshore and Polar Engineers
- offshore wind turbine, reliability- based design optimization (RBDO), dynamics, optimization design
- 2 in the last 30 days
- 24 since 2007
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The support structure of offshore wind turbines is working in harsh ocean environments, where uncertainties exist and affect the performance of the whole system. This work presents an efficient methodology for the Reliability Based Design Optimization (RBDO) of the support structure of offshore wind turbines considering uncertainties. Reliability analysis is a feasible option in the absence of field measurement data. Monte Carlo simulations are robust and used as reliability analysis benchmark, but they are very computationally demanding for offshore wind turbine cases. Efficient Fractional Moment reliability analysis method was proposed. The results show that the proposed methodology can obtain a reliable design with better dynamic performance and less weight. Compared with the deterministic optimization, the presented dynamic RBDO of offshore wind turbines is more practical, and this methodology can be applied in the design of other similar offshore structures.
The support structure of offshore wind turbines is working in harsh ocean environments, reliability analysis is a feasible option in absence of field measurement data (Yang et al., 2017). To ensure that the proposed offshore wind turbine design is cost effective, it is necessary to check whether the decided support structures provides optimal life cycle cost.
For a reliable design, it is essential to consider various uncertainties in the dynamic analysis of offshore wind turbine (Xiao and Yang, 2014; Zhang et al., 2017). Due to the random nature of environmental parameters, wave, wind and currents must be modelled as stochastic process (Zhang and Yang, 2014). Hence, there is a need of stochastic dynamic analysis on one hand and the need of developing performance assessment, maintenance and optimization of the offshore wind turbine system with uncertainties. We try to answer the following questions: a) Can we formulate an efficient and accurate method for reliability analysis to replace Monte Carlo simulations which are robust but too time consuming; b) How to overcome computational challenges associated with reliability-based optimization methodology of offshore wind turbine system?
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