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Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning

Author

Listed:
  • Lorin Jenkel

    (School of Engineering and Computer Science, Bern University of Applied Sciences, 2501 Biel, Switzerland)

  • Stefan Jonas

    (School of Engineering and Computer Science, Bern University of Applied Sciences, 2501 Biel, Switzerland
    Faculty of Informatics, Università della Svizzera italiana, 6900 Lugano, Switzerland)

  • Angela Meyer

    (School of Engineering and Computer Science, Bern University of Applied Sciences, 2501 Biel, Switzerland)

Abstract

A wealth of data is constantly being collected by manufacturers from their wind turbine fleets. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. Our study presents a privacy-preserving machine learning approach for fleet-wide learning of condition information without sharing any data locally stored on the wind turbines. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from accuracy gains from improved normal behavior models. Customizing the global federated model to individual turbines yields the highest fault detection accuracy in cases where the monitored target variable is distributed heterogeneously across the fleet. We demonstrate this for bearing temperatures, a target variable whose normal behavior can vary widely depending on the turbine. We show that no member of the fleet is affected by a degradation in model accuracy by participating in the collaborative learning procedure, resulting in superior performance of the federated learning strategy in our case studies. Distributed learning increases the normal behavior model training times by about a factor of ten due to increased communication overhead and slower model convergence.

Suggested Citation

  • Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6377-:d:1231866
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    References listed on IDEAS

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    1. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
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    3. Andrew Kusiak, 2016. "Renewables: Share data on wind energy," Nature, Nature, vol. 529(7584), pages 19-21, January.
    4. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    5. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
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    7. Haokun Fang & Quan Qian, 2021. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning," Future Internet, MDPI, vol. 13(4), pages 1-20, April.
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    9. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    10. Stefan Jonas & Dimitrios Anagnostos & Bernhard Brodbeck & Angela Meyer, 2023. "Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering," Energies, MDPI, vol. 16(4), pages 1-16, February.
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    Cited by:

    1. Davide Astolfi & Silvia Iuliano & Antony Vasile & Marco Pasetti & Salvatore Dello Iacono & Alfredo Vaccaro, 2024. "Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment," Energies, MDPI, vol. 17(24), pages 1-34, December.

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