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

Lorin Jenkel, Stefan Jonas () and Angela Meyer
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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
Angela Meyer: School of Engineering and Computer Science, Bern University of Applied Sciences, 2501 Biel, Switzerland

Energies, 2023, vol. 16, issue 17, 1-29

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.

Keywords: wind energy; federated learning; wind turbine fleets; condition monitoring; fault diagnostics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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