Abstract: The probabilistic analysis of Offshore Wind Turbines (OWT) is not a new practice. The standards for designing OWT (IEC 61400 class) emphasizes that assessing uncertainty is of major importance inside the design chain. Still, major challenges related to the uncertainty and the probabilistic assessment pose to the sector and its development. The analysis of operational loads is one them. The problem of analyzing extreme responses or cumulated damage in operation during the design phase is significantly related to its high computational cost. As we progressively add complexity to the system to account for its uncertainties, the computational effort increases and a perceptive design becomes a heavy task. If an optimization process is then sought, the designing effort grows even further. In the particular case of fatigue analysis, it is frequent to not be able to cover a full lifetime of simulations due to computational cost restrictions. The mentioned difficulties fomented the utilization of surrogate models in the reliability analysis of OWT. From these surrogate approximations the ones based on Kriging models gained a special emphasis recently for structural reliability. It was shown that, for several applications, these models can be efficient and accurate to approximate the response of the system or the limit state surfaces. The presented paper tackles some of the issues related to their applicability to OWT, in a case specific scenario of the tower component subjected to operational fatigue loads. A methodology to assess the reliability of the tower component to fatigue damage is presented. This methodology combines a Kriging model with the theory of extreme values. A one-dimensional Kriging case using the state of art NREL’s monopile turbine is presented. The reliability of the OWT tower is calculated for 20 years. The results show that the usage of a Kriging model to calculate the long term damage variation shows a high potential to assess the reliability of OWT towers to fatigue failure.

1. Structural probabilistic assessment of Offshore Wind Turbine
operation fatigue based on Kriging interpolation
Rui Teixeira, Alan O’Connor and Maria Nogal
Department of Civil, Structural and Environmental Engineering, Trinity
College Dublin, Dublin
James Nichols and Mark Spring
Lloyd’s Register, United Kingdom
ESREL2017, 21st June 2017
This project has received funding from the European Union’s Horizon 2020 research and innovation programme
under the Marie Sklodowska-Curie grant agreement No. 642453

2. Outline
• Introduction
• Offshore wind turbine fatigue design methodology
• Structural probabilistic assessment of Offshore Wind
Turbine operation fatigue based on Kriging interpolation
• Conclusions
• Further developments

3. Why is Offshore Wind Turbines development important?
• Global warming is a scientific fact.
• Renewable energy needs to be “pulled” forward.
• EU targets of 20% of Renewable Energy in total energy consumption by 2020
and 27% by 2030 (EU Commission).
• Projections of >2°C increase in global temperature by 2100 compared with pre-
industrial levels (Paris Agreement).
Source: Siemens 2014 report

4. Introduction
• Probabilistic analysis of the design of Offshore Wind Turbine (OWT)
towers
• OWT Towers fatigue life
• OWT are highly complex
systems that are affected by
multiple sources of
uncertainty. Demand a
strong reliability design
basis.
Why?

5. Offshore wind turbine fatigue design methodology
• Computationally demanding task.
• Current methodology involves the
extrapolation of load cycles using a
Peak-Over-threshold approach. Below
the threshold level loads are accounted
deterministically (IEC 61400-3).
• Still they may account for substantial damage for low SN slope (m) materials. High
damage generated by small amplitude loads.
• Ok for e.g. blades.
• Not ok for e.g. tower.

6. • Using Kriging surrogate models to design towers to fatigue
• Short term damage follows a Lognormal
distribution.
• Kriging is an interpolation model that
considers Gaussian uncertainty in the
interpolation.
Probabilistic optimization of the design of OWT towers
• Applied to decrease computational cost.
• Potential of application for reliability is very
high.

7. Probabilistic optimization of the design of OWT towers
Loads tower
sections.
NREL FAST
Monopile OWT
Rainflow
counting,
damage
summation and
statistical
characterization
of short-term
damage.
Kriging
surrogate
model.
Long term
fatigue
damage
distribution.
Statistically accurate?
No
Yes

8. Results - Structural probabilistic assessment of Offshore WindTurbine operation
fatigue based on Kriging interpolation
• 100 evaluations of 20 years damage
(time demanding).
• Very low probability of failure NREL
5MW monopile OWT.
• Variations of short term
damage with the wind are
very high.
• 72 evaluations to create the
model + 60 evaluations to
correct it. Validated with m-
life.

9. Conclusions
• A new approach was implemented to design OWT to fatigue design. It is a “non-
intrusive” methodology.
• The Gaussian properties of the Kriging surface are adequate for simulating
short term damage on the OWT tower.
• Using the Kriging to approximate the damage surface of the model can cut
computational time from the design analysis.
• OWT fatigue design is a very computer resource consuming task.
• Using the estimation of error in some points of the DoE to correct the prediction
is valid option but that needs more research.
• It demands many additional simulations to converge the statistical
distributions.

10. Further Developments:
• Stochastic sensitivity of the Design of Experiments (DoE).
• No additional complexity should be considered in the model if the variable
does not account for important information in regard of OWT tower damage.
• Adaptive Design of Experiments when creating the surrogate model.
• Allows to optimize the ratio cost/accuracy when creating the surrogate model.
• Study the influence of deterministic prediction in DoE when creating the
surrogate model.
• Nevertheless, interesting potential to design and optimize the design of OWT
towers to operational fatigue.

11. Thank you!
Rui Teixeira rteixeir@tcd.ie
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Marie Sklodowska-Curie grant agreement No. 642453