Probabilistic temporal extrapolation of fatigue  damage of offshore wind turbine substructures  based on strain measurements

Hübler, Clemens; Rolfes, Raimund

doi:https://doi.org/10.5194/wes-7-1919-2022

Articles | Volume 7, issue 5

https://doi.org/10.5194/wes-7-1919-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-7-1919-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 7, issue 5

Research article

|

26 Sep 2022

Research article |

| 26 Sep 2022

Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements

Clemens Hübler and Raimund Rolfes

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Final revised paper (published on 26 Sep 2022)
Preprint (discussion started on 30 Mar 2022)

Interactive discussion

Status: closed

RC1:
'Comment on wes-2022-15', Anonymous Referee #1, 05 May 2022
The paper deals with a timewise extrapolation of fatigue damage measured with strain gauges at the tower of an offshore wind turbine. The paper compares three approaches: simple extrapolation, binning method, machine learning. Although the methods itself are not new and have been published before, the paper is a useful contribution for comparison and validation of these approaches using a dataset of measurements. The research question is judged to be relevant and of high interest for industry within the context of aging offshore wind assets. The paper is very well written, easy to follow and well presented, a pleasure to read. However, I have three major concerns about the content of the paper:

A main result of the paper is that ‘wind speed’ is the most important parameter for temporal extrapolation of fatigue damage. An important part of the storyline is that the paper uses data which is readily available over the life of the offshore wind turbine. However, the authors used mostly data from the met mast FINO1 to determine the wind speed. Most wind farms do not have access to a nearby met mast. The content of the paper is only useful, if the parameter ‘wind speed’ is purely extracted from SCADA (with acknowledgement of its limitations). Can you add the results for wind speed based on SCADA only?

The authors use environmental conditions and the turbine status for damage extrapolation. However, they do not consider continuous operational SCADA data, such as power output and pitch angle (which may even be more robust parameters than wind speed). Can you explain why you do not take this data into account?

I disagree with the conclusion that long-term extrapolation is possible even if the OWT has been modified. This may be the case for the example of this paper but a generalization can be wrong and dangerous. A change of operational conditions can have a significant effect on tower loads. For example, aerodynamic imbalance due to blade pitch misalignment increases tower loads. Such an imbalance may occur after some years of operation (e.g. after end of the measurement campaign) and stays often undetected. If such factors are not represented in the measurement period, the extrapolation will underestimate loads. Being unaware of such effects is, in my opinion, the largest limitation of the presented extrapolation approach.

Please also see my detailed comments in the pdf attached.
Citation: https://doi.org/10.5194/wes-2022-15-RC1
RC2:
'Comment on wes-2022-15', Anonymous Referee #2, 15 May 2022
This well-written paper refers to fatigue damage accumulation issues to predict/extend the future fatigue life of wind turbine substructures using monitoring data. The topic is very important and it is amazing that there is not more concern and scientific activity in this domain.

While the paper is focusing on fatigue action effects in WT towers due to wind action and issues of fatigue damage accumulation using the linear accumulation theory, the following items deserve at least some discussion in order to emphasize more the importance of fatigue damage calculations:

The WT industry claims for a fatigue lifetime of substructures of only 20 years which is ridiculous from technical and environmental viewpoints. Private companies are obviously not interested in durable infrastructure; replacing WT every 20 years is very profitable for companies but has a very negative impact on the environment. The authors are invited to comment on the issue of useful service life to be expected from (environmentally friendly) WT.

Measured strain (stress) values in WT towers show relatively small values but the number of stress cycles is very high. The fatigue issue related to WT towers is in the domain of Very High Cycle Fatigue. This should be discussed. F.ex., fracture mechanics based fatigue theories indicate a threshold of fatigue stress intensity at (welded) details for which stresses, beyond this threshold, do not contribute to fatigue damage. This item should be discussed in more detail and considered in the fatigue damage accumulation.

Related to the previous comment, typical (welded) details in WT towers and their fatigue resistance should be discussed in more detail, because of the obvious link between linear fatigue damage accumulation and the assumed S-N curve of fatigue vulnerable details.

A safety factor is introduced but it is not discussed on how a safety margin should be fixed for the present case of monitoring-based fatigue assessment approach.
Citation: https://doi.org/10.5194/wes-2022-15-RC2
AC1: 'Comment on wes-2022-15', Clemens Hübler, 30 May 2022

We want to thank all both reviewers for their comprehensive reviews that helped us to improve the quality of the paper especially with respect to a more detailed discussion of the short-term fatigue calculation and of the most relevant environmental and operational conditions.
Please, find attached our answers to the comments.
A revised version of the manuscript is prepared and the corresponding paragraphs are marked in the manuscript.

Citation: https://doi.org/10.5194/wes-2022-15-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Clemens Hübler on behalf of the Authors (30 May 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (14 Jun 2022) by Michael Muskulus

RR by Anonymous Referee #1 (27 Jun 2022)

RR by Wout Weijtjens (30 Jun 2022)

Suggestions for revision or reasons for rejection

Dear authors,

Thank you for your submission, I found it very interesting and a relevant study. For clarity I was brought into this as a reviewer at a later stage in the reviewing process. I've reviewed the paper 'as is' without reviewing your replies to other reviewers. I'm also familiar with your past work.

In general this paper is an ambitious effort to apply different strategies for fatigue damage estimation in the context of lifetime extension (or optimized operation) of offshore wind farms. It touches on several techniques and several considerations to be made when using any of the proposed techniques. In essence the paper primary objective is to act as a 'practical' review paper of several techniques rather than to have the ambition to introduce many novel solutions. I welcome such an effort. The presented techniques and (most) considerations are IMO relevant and on-topic and it was interesting to see the results.

** Global comment **
My biggest concern with the paper in the current form is a bit the downside of its ambitious nature. The large number of techniques used, intermingled with one-another, sometimes makes it difficult to keep track of the exact methodology followed. In particular this plays a role when the performance and the uncertainty are discussed. Questions like : "Are we looking at the error on the total damage (sum over a year) or at the 10 minute errors on damage?)", "Do we retrain the ANN for every iteration of our 1000 uncertainty assessments?" regularly popped up.
At times it was hard to follow what the boxplot meant that were presented to us. I feel some extra clarifications here and there how some methods are exactly employed might be beneficial. In particular when uncertainties are addressed.

Perhaps it might be a consideration to drop certain segments (e.g. the one on computation time felt unnecessary in my book) or even reduce the role of GPR (which due to their timely exit, I don't feel ended up contributing much to the paper) in favor of a more clear narative. But I leave this up to the authors.

** Specific comment **
(Line numbers are used approximately, some comments are later resolved, for those I add an UPDATE to my comment )
- How the authors relate their work to e.g. the work of the group of Prof. John D Sørensen? Who approaches this problem more from a probabilistic angle?
- One concern when working with 10 minute damages is the role of long term fatigue cycles, (E.g. See "Marsh e.a. - 2016 - Review and application of Rainflow residue process" and recently confirmed in "Sadeghi e.a. - 2022 - Fatigue damage calculation of offshore wind turbines' longterm data considering the low-frequency fatigue dynamics"). Obviously such information is lost when binning as the temporal sequence is lost, moreover it can be difficult to replicate when drawing from a random distribution in forecasting. A similar concern applies for sequence effects. Do the authors have any thoughts on the matter?
- Line 132 : authors state that mean values are not relevant for fatigue damage. In fact they are saying to ignore mean stress effects, this is a (valid) assumption, but I wouldn't say mean values aren't relevant.
- Figure 8, 9: Scatter plots with such a big dataset, don’t show the density differences of the data which I assume is more densely packed towards the middle of the cloud. Perhaps working with a different style plot.
- How does 315 relate to the dominant wind direction?
- Line 215 : the discussion on the “horizontal part” is interesting, but perhaps is phrased a bit too simplistic given there is a common terminology for it: ‘Fatigue limit’. Perhaps a wording “the S-N curve does not account for a fatigue limit in the material, i.e. a horizontal part at low stress cycles, …” is more appropriate as I do appreciate the authors’ effort of linking it with with such a strong visual cue
- Line 280 : I personally find the statement “fill up about 40% of the bins (cf. Fig.9)” overly dramatizes the problem. Yes Figure 9. Does show a lot of “empty space” but in fairness those missing conditions also represent improbable conditions (e.g. very high waves at low wind speed), so while 40% of the bins might be empty, their combined probability will lay well below 40%, probably even reasonably below 1%. The story of empty bins is correct, but might not have too much impact on the total outcome due to low probabilities.
- Eq 13: the authors opt to work with a absolute error on their prediction. But I’m curious to see the results with a sign. E.g. given the conservative nature of filling empty bins do we end up with a conservative damage estimate? In contrast ANN doesn’t have this built in conservatism, does it still produce conservative predictions (UPDATE: they later present results without a signed error)
- Figures 14: just to be 100% sure, each box in the boxplot only contains 13 samples?
- Figure 15 : is it possible to have the axis the same size as the adjacent Figure 14? I feel it is fair to compare these results.
- In section 4.1.4: for the binning and functional extrapolation, I assume you are still using the wind speeds for binning and extrapolation? Perhaps this should be emphasized a bit stronger.
- Section 4.1.4. : how are the “discrete” status’s fed into the ANN? As numeric values (operation=1, parked=2)?
- Section 4.2 : I’m a bit confused about this whole segment. For the binned technique you explicitly mention that the processing times goes up dramatically because you need to fill in the empty bins. I conclude that in your 1000 prediction, for every prediction you refill the bins and calculate the means of each bin? (i.e. this is the training phase of the binning method, retraining the damage map for every iteration) Do you do something similar for the ANN and GPR? So each iteration drawing new training data and then fitting a new model to said training data, so basically training a 1000 different models? Or are you just 1000 times evaluating into a single well performing model?
I then understand why computing time blows up. But if you did so, I don’t think I would ever consider the uncertainty of ANN by training 1000 models (without much oversight). I rather have an uncertainty come from feeding a small number of ANNs 1000 different validation datasets (e.g. drawn using bootstrapping) and quantify the uncertainty from those predictions.
If you don’t train an ANN for every evaluation then it is perhaps an unfair comparison as the training of the binning strategy is building these damage lookup tables.
- Figures 20 and 21 : how come that in Figure 20 the binned strategy is non-conservative (predicted damage > actual damage) and in figure 21 it is the other way around? Where the empty bins not conservative enough?
- Figure 25 : the role of when the measurements start is an interesting comment (and understandable), I assume a similar mechanism may also reduce the amount of training data required for the ANN to converge?

These are my comments, I hope they are clear and help to further improve the current work.

Kind regards,

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ED: Reconsider after major revisions (04 Jul 2022) by Michael Muskulus

AR by Clemens Hübler on behalf of the Authors (05 Aug 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (24 Aug 2022) by Michael Muskulus

ED: Publish as is (27 Aug 2022) by Paul Veers (Chief editor)

AR by Clemens Hübler on behalf of the Authors (29 Aug 2022)

Short summary

Offshore wind turbines are beginning to reach their design lifetimes. Hence, lifetime extensions are becoming relevant. To make well-founded decisions on possible lifetime extensions, fatigue damage predictions are required. Measurement-based assessments instead of simulation-based analyses have rarely been conducted so far, since data are limited. Therefore, this work focuses on the temporal extrapolation of measurement data. It is shown that fatigue damage can be extrapolated accurately.