the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing lidar-assisted feedforward and multivariable feedback controls for large floating wind turbines
David Schlipf
Abstract. We assess the performances of two control strategies on the IEA 15 MW reference floating wind turbine through
OpenFAST simulations. The multivariable feedback controller tuned by the toolbox of the Reference Open Source Controller
(ROSCO) is considered the benchmark for comparison. We then tune the feedback gains for the multi-variable controller,
considering two cases: with and without lidar-assisted feedforward control. The tuning process is performed using OpenFAST
simulations considering realistic offshore turbulence spectral parameters. We reveal that the optimally tuned controllers are
robust to changes in turbulence parameters caused by atmospheric stability variations. Compared to the baseline multivariable
controller, the one with optimal tuning significantly reduced the tower damage equivalent load, which results in a lifetime
extension of 19.2 years. With the assistance of feedforward control provided by a typical four-beam lidar, compared with the
optimally tuned MVFB control, the lifetime of the tower can be extended by 5.1 years.
Feng Guo and David Schlipf
Status: open (until 14 Apr 2023)
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RC1: 'Comment on wes-2023-9', Anonymous Referee #1, 22 Mar 2023
reply
The work is interesting and the manuscript is well written. It could be further improved by:
(1) Adding a nomenclature section at the beginning of the document.
(2) Adding a block diagram of the control structure.
(3) Providing some information on the baseline feedback control. Maybe some step simulations or the frequency response of the open loop and closed loop system.
(4) Providing some more data about the numerical optimization. How did you choose the total amount of simulations, the step in the different values, etc?
Some other corrections on typos and grammar are attached in the pdf.
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RC2: 'Comment on wes-2023-9', Anonymous Referee #2, 24 Mar 2023
reply
The authors presented several implementations of controllers on a large scale floating offshore wind turbines, a baseline controller from the NREL, a slight modification of the baseline controller on the floating feedback with a fine tuning/optimization of the feedback gains called MVFB, and the MVFB controller in combination with a feedforward LiDAR assisted controller. From what I have understood, most of the innovation lies in the combination of existing blocks from the literature, the slight modification on the floating feedback, the implementation on a large scale floating offshore wind turbine and the optimization/fine tuning of the parameters. If there is more innovation than this, it seems unclear to me.
The authors successfully implemented these controllers and the MVFB controller in combination with the feedforward LiDAR assisted controller performs better than the MVFB controller alone, which performs better than the baseline controller.
The paper is quite well written, understandable and the presentation of the results is good.
However, here are few remarks:
- The authors say throughout the paper that they are optimizing parameters of the orientation of the LiDAR beams and of the controllers, but I did not understand what optimization they were performing, as I did not spot any cost function, constraints or optimization algorithm. Therefore, I understand it more as a sequential fine tuning of parameters illustrated by the figures in the paper, and if it is, I guess a scheme summarizing in what order are the parameters optimized/tuned and how would be of great help for the clarity of the paper.
- In line 123, the authors say that they choose a time step of 0.293 seconds, it is a very precise number, and I wonder how did you chose it?
- In lines 267 and 400, the word "overspend" is used, and I was wondering if it was a typo for "overspeed", or if it is not, can you define the word a little bit more, as this word is quite unusual to me.
- In line 340, the authors state that the use of more advanced control strategies such as model predictive control should improve the performance, could you please develop and justify further your statement?
- In equation (11), the definition of the extended lifetime looks quite akward, first of all you could have written it more concisely as: EL=20((DEL_j/DEL_i)^m - 1) . Moreover, the use of this metric looks quite unusual to me, could you please explain where does it come from or a cite a source first using it?
Moreover, I am not a native english speaker, but here are some typos/english mistakes I have spotted:
- line 3: "is considered the benchmark" --> "is considered as a benchmark"
- line 101: "more large" --> "larger"
- line 179: "transnational" --> "translational"
- line 209: "butter time" --> "buffer time"
- line 247: "values vary from 10 to ..." --> "values varying from 10 to ..."
- line 316: "values fullfill the criteria" --> "values fullfilling the criteria"
- line 357: "is slight higher than" --> "is slightly higher than"
- line 391: "tends underestimate" --> "tends to underestimate"
- line 415: "which can potential cause more damage" --> "which can potentially cause more damage"
There might be a few others, therefore I strongly recommend the authors to carefully proofread their papers once again.
Citation: https://doi.org/10.5194/wes-2023-9-RC2
Feng Guo and David Schlipf
Feng Guo and David Schlipf
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