5 papers · 1 filter
Generative multi-domain transfer learning for fault detection in data-scarce wind turbines
Stefan Jonas, Angela Meyer
Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require sufficient fault-free training…
Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning
Stefan Jonas, Angela Meyer
Intelligent condition monitoring of wind turbines is essential for reducing downtimes. Machine learning models trained on wind turbine operation data are commonly used to detect an…
Wind turbine condition monitoring based on intra- and inter-farm federated learning
Albin Grataloup, Stefan Jonas, Angela Meyer
As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and do…
Bias correction of wind power forecasts with SCADA data and continuous learning
Stefan Jonas, Kevin Winter, Bernhard Brodbeck +1
Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessa…
A review of federated learning in renewable energy applications: Potential, challenges, and future directions
Albin Grataloup, Stefan Jonas, Angela Meyer
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and ent…