Risk-based planning of O&M for wind turbines using physics of failure models
Wind turbines are in some countries contributing significantly the production of electricity. For offshore wind turbines reliability is a key issue since costs to operation and maintenance may be significant contributors to the Levelized Cost Of Energy and OM costs are highly dependent on the reliability of the components implying that it is important to focus on increasing the reliability as much as is economically reasonable. This paper describes aspects for reliability analysis of wind turbines with special focus on structural components, especially the wind turbine blades. In many wind turbine components deterioration processes such as fatigue, wear and corrosion may result in failures, for example in welded details, blades, bearings and gearboxes. In many cases it may be possible to detect the damages before actual failure, and thereby perform preventive maintenance instead of corrective, expensive repair/ maintenance. This requires some type of condition monitoring to give information on the condition of the components. It can either be online monitoring or manual inspections. The use of preventive maintenance can possibly reduce the costs, as repairs can be cheaper to perform before actual failure, and because the downtime will be shorter compared to corrective maintenance. On the other hand, preventive maintenance leads to more repairs in total, and optimally the maintenance effort should be adjusted to minimize the total expected costs. In this paper it is described how risk-based methods can be used to optimally plan operation & maintenance using Bayesian decision theory adapted to offshore wind. An illustrative example is presented considering wind turbine blades and using the reference wind farm in the NORCOWE research project.
How to Cite
Risk blade maintenance degradation
NORCOWE reference wind farm. https://rwf.computing.uni.no/
Nissim, M. (2013). Blade Maintenance for Reliability, an Owner/Operator Perspective. Presentation. EDP renewables
Sørensen, J. D. (2013). Risk and Reliability in Engineering. Course. Aalborg University.
Florian, M., Sørensen, J. D. (2015). Wind Turbine Blade Life-Time Assessment Model for Preventive Planning of Operation and Maintenance. Journal of Marine Science and Engineering 2015, 3, 1-x manuscripts; doi:10.3390/jmse30x000x
Sørensen, J.D., Frandsen, S., & Tarp-Johansen, N.J. (2008). Effective turbulence models and fatigue reliability in wind farms. Probab. Eng. Mech. 2008, 23, 531–538.
Jonkman, J.M., Buhl, M.L., Jr. (2005). FAST User’s Guide. Technical Report. National Renewable Energy Laboratory. Golden, CO, USA, 2005.
Jensen, F. W., & Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs. LLC, 233 Spring Street, New York, NY 10013, USA: Springer Science + Business Media.
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