the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A machine learning-based approach for active monitoring of blades pitch misalignment in wind turbines
Abstract. In recent years, timely anomaly detection in wind turbine operations, especially offshore, has become critical. Yet, promptly identifying faults and damages remains a significant challenge, leading to costly maintenance and consuming resources. Rotor blade pitch misalignment is a critical issue, causing downtime and reduced energy production. Traditional inspection methods are resource-intensive, time-consuming, and also struggle to identify the specific misaligned blades. In addition, their accuracy degrades in case of small misalignments and strongly depends on the wind regimes, as they are less reliable in turbulence. The absence of an effective automatic solution persists, requiring costly on-site verification.
To tackle this challenge, this paper introduces a novel machine-learning-based approach for automatic pitch misalignment detection and localization. This approach not only localizes the affected blades but also detects small misalignments as low as 0.1 degrees. The methodology relies on features extracted from a limited set of sensors already integrated into modern wind turbine systems. Specifically, the extracted indicators are designed to effectively integrate frequency and time-domain information on turbine operating conditions, enabling high detection performance even in turbulent wind regimes.
The approach is validated across an extended operational envelope using data gathered from a state-of-the-art simulation model commonly used for designing and certifying commercial wind turbine systems.
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RC1: 'Comment on wes-2024-100', Anonymous Referee #1, 07 Oct 2024
A machine learning-based approach for active monitoring of blades pitch misalignment in wind turbines (Milani et al. 2024)
Summary Comments:
Thank you for the opportunity to review this paper.
As discussed in this study, detecting misalignment of turbine blades is complex yet a key component to successful wind energy implementation, especially for offshore wind or in areas where maintenance is more difficult and costly. This study is an important contribution to the field, as it introduces a new method for detection of wind turbine misalignment, and more so, advances on any previous literature by the ability to detect which blades are misaligned. This paper is well written, and nicely walks the reader through their methodology; proving how this approach is more advanced than previously existing methods.
Overall, I think this paper has a lot to contribute to the field and demonstrates thorough analysis. My below comments are suggestions that I believe will help to improve the clarity of the paper.
Major Comments:
Line 69: My one main comment/concern for this paper is the reproducibility. You mention that your method uses a "minimal set of sensors", but you never mention which sensors/measurements you use from the wind turbine. Without a clear discussion of what in-situ measurements are needed, this method will not be able to be reproduced or tested in the field.
Minor Comments:
Line 15-20: You may want to add a brief discussion about what typical misalignment ranges are and how frequently this occurs on a given turbine or within an array. Does turbine misalignment decrease energy production? Also, is there a significant threshold where turbine misalignment becomes more of an issue/concern?
Line 147: Would it be beneficial to show the power curve here?
Line 155: What ranges of wind speeds are you considering? Is there a difference in algorithm performance when the turbine is just above cut-in vs. at rated capacity?
Figures 2 and 3: I would recommend adding (a) and (b) to each figure. It will improve clarity when references the figures in the text.
Citation: https://doi.org/10.5194/wes-2024-100-RC1 -
AC1: 'Reply on RC1', Sabrina Milani, 25 Oct 2024
Dear Referee #1,
thank you very much for your feedback and valuable comments on our paper, “A machine learning-based approach for active monitoring of blades pitch misalignment in wind turbines” . Please find attached a detailed response addressing each of your comments.
Best Regards,
Sabrina Milani, on behalf of all Authors.
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AC1: 'Reply on RC1', Sabrina Milani, 25 Oct 2024
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RC2: 'Comment on wes-2024-100', Anonymous Referee #2, 15 Oct 2024
Thank you for the opportunity to review this paper. Also, thank the authors for submitting this well written manuscript.
The paper presents a novel machine learning-based approach for automatic pitch misalignment detection and localization in wind turbines. As marked in the manuscript, the misalignment of blades is usually difficult to inspect and detect (especially for the offshore turbine systems). The reported approach can not only predict the exact entity of the misalignment, and localize the affected blades with high accuracy, but also only relies on features extracted from a limited set of sensors already integrated into modern wind turbine systems. This ensures the well-tuned model can be applied easily to modern turbine systems.Overall, I consider this manuscript well written with clear overview of the addressed problem and provided contribution. The methodology used in the manuscript is well designed and presented. I only have one question and several minor comments below that I would like to get discussed and addressed by the authors before publication.
Questions:
I noticed that the manuscript focuses on the scenarios with unbalanced blade misalignments. These unbalanced cases ensure the Yawing moments differ from the healthy case. I was wondering if the Yawing moments will still be at the non-N Rev positions when the three turbine blades have the same amount of misalignments. In this case, can the proposed ML model make accurate predictions?
Minor Comments:
- I recommend adding the explanation of “V21” (wind speed 7m/s) and “V07” (wind speed 21m/s) in the caption of the Figure 2 to increase the readability.
- I recommend editing Figure 6 to show the overlapping between two data points. The current figure has the predicted result covering over the comparing data, which cannot provide a clear view of the matching status of the two sets of data.
Citation: https://doi.org/10.5194/wes-2024-100-RC2 -
AC2: 'Reply on RC2', Sabrina Milani, 25 Oct 2024
Dear Referee #2,
thank you very much for your feedback and valuable comments on our paper, “A machine learning-based approach for active monitoring of blades pitch misalignment in wind turbines” . Please find attached a detailed response addressing each of your comments.
Best Regards,
Sabrina Milani, on behalf of all Authors.
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RC3: 'Comment on wes-2024-100', Anonymous Referee #3, 18 Oct 2024
Please find my comments on this paper in the attached document.
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AC3: 'Reply on RC3', Sabrina Milani, 25 Oct 2024
Dear Referee #3,
thank you very much for your feedback and valuable comments on our paper, “A machine learning-based approach for active monitoring of blades pitch misalignment in wind turbines” . Please find attached a detailed response addressing each of your comments.
Best Regards,
Sabrina Milani, on behalf of all Authors.
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AC3: 'Reply on RC3', Sabrina Milani, 25 Oct 2024
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