Fatigue damage reduction in hydropower startups with machine learning

Till Muser, Ekaterina Krymova (), Alessandro Morabito, Martin Seydoux and Elena Vagnoni
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Till Muser: EPFL & ETH Zürich
Ekaterina Krymova: EPFL & ETH Zürich
Alessandro Morabito: École Polytechnique Fédérale de Lausanne
Martin Seydoux: École Polytechnique Fédérale de Lausanne
Elena Vagnoni: École Polytechnique Fédérale de Lausanne

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract As the global shift towards renewable energy accelerates, achieving stability in power systems is crucial. Hydropower accounts for approximately 17% of energy produced worldwide, and with its capacity for active and reactive power regulation, is well-suited to provide necessary ancillary services. However, as demand for these services rises, hydropower systems must adapt to handle rapid dynamic changes and off-design conditions. Fatigue damage in hydraulic machines, driven by fluctuating loads and varying mechanical stresses, is especially prominent during the transient start-up of the machine. In this study, we introduce a data-driven approach to identify transient start-up trajectories that minimize fatigue damage. We optimize the trajectory by leveraging a machine learning model, trained on experimental stress data of reduced-scale model turbines. Numerical and experimental results confirm that our optimized trajectory significantly reduces start-up damage, representing a meaningful advancement in hydropower operations, maintenance, and the safe transition to higher operational flexibility.

Date: 2025
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DOI: 10.1038/s41467-025-58229-z

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