Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning

Abstract

Terabytes of data are collected by wind turbine manufacturers from their fleets every day. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. We present a distributed machine learning approach that preserves the data privacy by leaving the data on the wind turbines while still enabling fleet-wide learning on those local data. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from more accurate 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 turbine experiences a loss in model performance from participating in the federated learning process, resulting in superior performance of the federated learning strategy in our case studies. The distributed learning increases the normal behavior model training times by about a factor of ten due to increased communication overhead and slower model convergence.

Added: case study results for data from a different fleet; distribution shift discussion; formatting and presentation changes. Original results remain unchanged

References in corpus (5)

• Towards Personalized Federated Learning
• Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption
• Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations
• Personalized Federated Learning using Hypernetworks
• Image encryption for Offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams

Cited by in corpus (3)

• A review of federated learning in renewable energy applications: Potential, challenges, and future directions
• Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning
• Wind turbine condition monitoring based on intra- and inter-farm federated learning