Machine learning for the components fatigue prediction in hydropower generation

July 1, 2022
In Progress
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The contribution of renewable energy has increased dramatically in Europe, now estimated to be greater than 33%. As part of the European Green Deal, the European Commission is currently raising its targets still further with significant contribution from intermittent renewables, such as wind and solar sources, and disconnection of the so-called conventional units, as greenhouse gases emitters. This drastic change in the power system calls for a strong support from sources to guarantee the frequency and voltage stability by providing ancillary services for a suitable power regulation in both production and consumption. Hydropower easily provides active and reactive power regulation, but the need of ancillary services provision is expected to grow considerably in the next decades. Facing the increasing demand in flexibility, the hydroelectrical technologies employed in the power plants have to be ready to tackle the foreseen challenges in following fast dynamics. This often requires hydraulic machines to operate in off-design conditions and to increase the number of transitions to ensure high flexibility, but also availability and reliability of the power system.

PAIRED-HYDRO aims at developing a methodology to improve the operational safety and the condition-based maintenance asset of the hydroelectric unit. In particular, the study will be focused on the prediction of the damage due to fatigue and the optimization of the operational sequences to minimize the lifetime reduction of runner blades and guide vanes of a Francis-type hydraulic machine (see Figure 2).



SDSC Team:
Guillaume Obozinski
Ekaterina Krymova
Till Muser

PI | Partners:

EPFL, Technology Platform for Hydraulic Machines:

  • Dr. Elena Vagnoni
  • Dr. Alessandro Morabito
  • Prof. Mario Paolone

More info



Currently, there exists no modelling of the stress or damage incurred in turbines, thus predictive maintenance is used: At regular intervals, the hydroelectric unit is stopped for inspection and reparis. The goal of the project is to a) provide an accurate model of damage incurred both in the steady-state and transient regime of hydraulic turbines; and b) to provide a method that produces favourable (i.e. least damaging) start-up trajectory, allowing for a more judicious maintenance schedule and less frequent repairs.

Proposed Approach / Solution

The SDSC models the stress inflicted on different parts of the turbine based on operating conditions. The stress signal can be converted into a quantification of damage using standard mechanical engineering techniques such as rainflow counting and Miner’s rule. As oscillations are non-negligible contributors to the damage, we take care to model oscillations as well as an overall trend estimate (see Figure 1). After training and validating the stress model, we investigate optimized controls sequences to produce less-damaging start-up trajectories. These trajectories have been tested using a reduced-scale model at the PTMH (see Figure 3) to verify their performance and incure less damage than previously investigated trajectories.

Figure 1: Comparison of true signal and estimate produced by the model. The constructed model estimates a trend based on the current controls. Additionally, the oscillations are similarly modelled based on the controls, but in Fourier space. The complete reconstructed signal aims to be a faithful approximation across the frequency spectrum.
Figure 2: Runner of a Francis turbine. Water flowing through the runner causes it to spin, powering a generator. Francis turbines are the most commonly used turbines today and can achieve over 90% efficiency.
Figure 3: Schematic of the test rig at the PTMH. The rig allows for testing reduced-scale hydraulic machines at various operating conditions and was used to collect the data for this project.


Hydropower already accounts for almost 60% of Swiss energy production, which is likely to increase with a shift to renewable energy in the future. By improving the life expectancy of hydraulic turbines, this project will lower the cost of maintenance and replacement of hydropower plants. Through collaboration with hydropower plant utilities such as EDF Hydro, the results of this project could find direct application in a full-size hydropower plant.



Additional resources


  1. S.W. Gregg, J.P.H. Steele, and D. L. Van Bossuyt, 2017, Machine Learning: A Tool for Predicting Cavitation Erosion Rates on Turbine Runners, Hydro Reviews, 36(3).
  2. A. Lyutov, A. Kryukov, S. Cherny, D. Chirkov, A. Salienko, V. Skorospelov and P. Turuk, 2016, Modelling of a Francis Turbine Runner Fatigue Failure Process Caused by Fluid-Structure Interaction, IOP Conf. Series: Earth and Environmental Science, 49.
  3. A. Heng, S. Zhang, A.C.C. Tan, J. Mathew, 2009, Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, 23(3) 724 – 739.
  4. M. A. Miner, 1954, Cumulative damage in fatigue. J. Applied Mechanics, 12 159 – 64.
  5. M Seydoux et al, Assessments of hydropower plants start-up sequences and equivalent runner damage under transient operation, IOP Conference Series: Earth and Environmental Science, 2022


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