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Condition monitoring and diagnostic of hydropower units based on machine learning techniques

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Listed:
  • Jad, Samy
  • Desforges, Xavier
  • Medjaher, Kamal
  • Villard, Pierre-Yves
  • Caussidéry, Christian

Abstract

As the energy sector accelerates its transition toward incorporating a greater proportion of renewable and low-carbon sources in the electricity mix, ensuring the availability of hydroelectric power plants is more crucial than ever to maintain the equilibrium between electricity supply and demand. This is particularly significant in Europe, where the ageing of power plant infrastructures raises concerns about the energy grid's ability to accommodate greater capacity volatility in the context of unstable and limited remaining hydro potential. For this reason, this paper introduces a diagnostic methodology based on machine learning techniques for detecting and characterising anomalous behaviours of hydro generators related to long-term degradation in the context of seasonal periodicity.

Suggested Citation

  • Jad, Samy & Desforges, Xavier & Medjaher, Kamal & Villard, Pierre-Yves & Caussidéry, Christian, 2025. "Condition monitoring and diagnostic of hydropower units based on machine learning techniques," Renewable Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:renene:v:249:y:2025:i:c:s0960148125008729
    DOI: 10.1016/j.renene.2025.123210
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    References listed on IDEAS

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    1. Kougias, Ioannis & Aggidis, George & Avellan, François & Deniz, Sabri & Lundin, Urban & Moro, Alberto & Muntean, Sebastian & Novara, Daniele & Pérez-Díaz, Juan Ignacio & Quaranta, Emanuele & Schild, P, 2019. "Analysis of emerging technologies in the hydropower sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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