The FleetCM4Wind project develops new AI methods to help wind farms operate more reliably and efficiently. Modern wind farms consist of hundreds of turbines that must work together under changing weather and operating conditions. Detecting faults early is difficult because data are scattered across many locations and mostly describe normal operation. FleetCM4Wind uses federated learning, a privacy-preserving approach that allows operators at different sites to train shared models without exchanging raw data. The project combines adaptive and decentralized learning to improve this collaborative training and enable reliable fault detection in turbines. Using open wind-turbine datasets and advanced simulations, the research will be carried out at Tampere University with national computing resources. The results will make renewable electricity production more dependable, and cost-effective, while strengthening Finland's and Europe's leadership in trustworthy AI for clean energy systems.

2026

2030