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Condition monitoring and predictive maintenance methodologies for hydropower plants equipment

Author

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  • Betti, Alessandro
  • Crisostomi, Emanuele
  • Paolinelli, Gianluca
  • Piazzi, Antonio
  • Ruffini, Fabrizio
  • Tucci, Mauro

Abstract

Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in Information and Communication Technologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. Also, we show that the proposed KPI outperforms conventional multivariable process control charts, like the Hotelling t2 index.

Suggested Citation

  • Betti, Alessandro & Crisostomi, Emanuele & Paolinelli, Gianluca & Piazzi, Antonio & Ruffini, Fabrizio & Tucci, Mauro, 2021. "Condition monitoring and predictive maintenance methodologies for hydropower plants equipment," Renewable Energy, Elsevier, vol. 171(C), pages 246-253.
    • Handle: RePEc:eee:renene:v:171:y:2021:i:c:p:246-253
    DOI: 10.1016/j.renene.2021.02.102
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    References listed on IDEAS

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    1. Li, Huanhuan & Xu, Beibei & Riasi, Alireza & Szulc, Przemyslaw & Chen, Diyi & M'zoughi, Fares & Skjelbred, Hans Ivar & Kong, Jiehong & Tazraei, Pedram, 2019. "Performance evaluation in enabling safety for a hydropower generation system," Renewable Energy, Elsevier, vol. 143(C), pages 1628-1642.
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    Cited by:

    1. Fatemeh Hajimohammadali & Emanuele Crisostomi & Mauro Tucci & Nunzia Fontana, 2024. "Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants," Energies, MDPI, vol. 17(22), pages 1-16, November.
    2. Murad, C.A. & Bellinello, M.M. & Silva, A.J. & Netto, A. Caminada & de Souza, G.F.M. & Nabeta, S.I., 2022. "A novel methodology employed for ranking and consolidating performance indicators in holding companies with multiple power plants based on multi-criteria decision-making method," Operations Research Perspectives, Elsevier, vol. 9(C).
    3. Aldemar Leguizamon-Perilla & Juan S. Rodriguez-Bernal & Laidi Moralez-Cruz & Nidia Isabel Farfán-Martinez & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortua, 2023. "Digitalisation and Modernisation of Hydropower Operating Facilities to Support the Colombian Energy Mix Flexibility," Energies, MDPI, vol. 16(7), pages 1-17, March.
    4. Yanfei Liu & Wentao Wang & Wenjun Wang & Chengbo Yu & Bowen Mao & Dongfang Shang & Yucong Duan, 2023. "Purpose-Driven Evaluation of Operation and Maintenance Efficiency and Safety Based on DIKWP," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    5. Lenio Prado & Marcelo Fonseca & José V. Bernardes & Mateus G. Santos & Edson C. Bortoni & Guilherme S. Bastos, 2023. "Forecast of Operational Downtime of the Generating Units for Sediment Cleaning in the Water Intakes: A Case of the Jirau Hydropower Plant," Energies, MDPI, vol. 16(17), pages 1-20, September.

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