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Fatigue damage reduction in hydropower startups with machine learning

Author

Listed:
  • 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)

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.

Suggested Citation

  • Till Muser & Ekaterina Krymova & Alessandro Morabito & Martin Seydoux & Elena Vagnoni, 2025. "Fatigue damage reduction in hydropower startups with machine learning," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58229-z
    DOI: 10.1038/s41467-025-58229-z
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    References listed on IDEAS

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