Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant

Modelo de Correlación Desgaste - Cantidad de Sedimentos para la Programación de Mantenimiento Preventivo de una central Hidroeléctrica

Published 2025-01-30


https://doi.org/10.37116/revistaenergia.v21.n2.2025.691
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Vol. 21 No. 2 (2025): Revista Técnica "energía", Edición No. 21, ISSUE II
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EFICIENCIA ENERGÉTICA
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Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant . (2025). Revista Técnica "energía", 21(2), PP. 39-47. https://doi.org/10.37116/revistaenergia.v21.n2.2025.691
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Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant . (2025). Revista Técnica "energía", 21(2), PP. 39-47. https://doi.org/10.37116/revistaenergia.v21.n2.2025.691

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Kleber Zhañay
Perfil Google Scholar
https://orcid.org/0009-0002-4413-3748 ORCID
Cristian Leiva
Perfil Google Scholar
https://orcid.org/0000-0002-8255-1337 ORCID
Erika Pilataxi
Universidad Politécnica Salesiana,
Perfil Google Scholar
https://orcid.org/0009-0009-2633-0407 ORCID
William Quitiaquez
PONTIFICIA UNIVERSIDAD CATOLICA
https://orcid.org/0000-0001-9430-2082 ORCID
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The present research is carried out for the improvement of the availability of a hydroelectric power plant through a wear-sediment quantity correlation model for the scheduling of its preventive maintenance, the data is based on the measurement of blade thicknesses, as well as visual inspection to identify discontinuities in the water equipment, once the data has been collected, data analysis techniques can be used to evaluate the condition of the Francis turbine and determine the need for preventive maintenance under working condition. The data analysis detailed is the least squares method where the independent variables considered are power and suspended particles with their nephelometric unit of measurement of turbidity in parts per million (PPM). By means of the aforementioned analysis, it is possible to complete the results with the projection of the wear to years after the data obtained from the inspection point, and it also allows taking preventive measures before a failure occurs, which helps to reduce downtime and maintenance costs. Thus, the hydroelectric power plant under study has an annual average availability of 97.21 %, reduced by the suspension of power generation due to reservoir flushing and scheduled maintenance shutdowns. While the annual average reliability is 99.89 %, it is reduced by unscheduled failures. The result of the correlation statistical model determined the preventive maintenance for improvement conditions of 98 % of the availability in the hydroelectric power plant and is reflected in the reduction of days of no electricity generation.

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  1. D. F. G. Pinargote, G. J. B. Sornoza, A. V. Pérez, and M. R. Gámez, “La generación distribuida y su regulación en el ecuador / The distributed generation and its regulation in ecuador,” Brazilian J. Bus., vol. 3, no. 3, pp. 2018–2031, 2021, doi: 10.34140/bjbv3n3-001.
  2. C. Sánchez, “DESGASTE DE MÁQUINAS HIDRÁULICAS EN LA GENERACIÓN HIDROELÉCTRICA,” Desarro. TECNOLÓGICO E INNOVACIÓN Empres., vol. 1, p. 17, 2016.
  3. H. Rojas, E. Duque, and Y. Garcia, “Contexto Actual del Sector Hidroeléctrico Ecuatoriano: Análisis de Proyectos Emblemáticos,” Grup. Ing. Sísmica y Sismol. , pp. 1–6, 2016, [Online]. Available: https://ingenieriasismica.utpl.edu.ec/sites/default/files/5.SectorHidricoEcuador.pdf
  4. G. Fonseca et al., “Financial compensation in hydropower generation: a tool for social and environmental development,” Water Policy, vol. 21, 2019, doi: 10.2166/wp.2019.007.
  5. de M. R. Billia and M. F. Fábio, “Influence of Sedimentation on Hydroelectric Power Generation: Case Study of a Brazilian Reservoir,” J. Energy Eng., vol. 141, no. 3, p. 4014016, Sep. 2015, doi: 10.1061/(ASCE)EY.1943-7897.0000183.
  6. J. Liu, J. Yu, and C. Jiang, “Evaluation on sediment erosion of Pelton turbine flow passage component,” IOP Conf. Ser. Earth Environ. Sci., vol. 240, no. 2, pp. 0–8, 2019, doi: 10.1088/1755-1315/240/2/022027.
  7. J. Chavarro, “Modelación y simulación en CFD de una turbina Kaplan,” 2018. doi: 10.13140/RG.2.2.19548.85123.
  8. S. Gutierrez, “Plan de mantenimiento basado en la metodología TPM para incrementar la productividad de los equipos línea amarilla en la empresa Renteq Maquinarias SAC,” Univ. Andin. del Cusco, pp. 1–118, 2020, [Online]. Available: http://repositorio.ucv.edu.pe/bitstream/handle/20.500.12692/47102/Gutierrez_RS-SD.pdf?sequence=1&isAllowed=y
  9. P. Tomczyk, B. Gałka, M. Wiatkowski, B. Buta, and Ł. Gruss, “Analysis of spatial distribution of sediment pollutants accumulated in the vicinity of a small hydropower plant,” Energies, vol. 14, no. 18, 2021, doi: 10.3390/en14185935.
  10. S. Sangal, M. Singhal, and R. P. Saini, “Hydro-abrasive erosion in hydro turbines: a review,” Int. J. Green Energy, vol. 15, pp. 1–22, 2018, doi: 10.1080/15435075.2018.1431546.
  11. J. Bogen and T. E. Bønsnes, “The impact of a hydroelectric power plant on the sediment load in downstream water bodies, Svartisen, northern Norway,” Sci. Total Environ., vol. 266, no. 1, pp. 273–280, 2001, doi: https://doi.org/10.1016/S0048-9697(01)00650-7.
  12. A. K. Rai, A. Kumar, and T. Staubli, “Effect of concentration and size of sediments on hydro-abrasive erosion of Pelton turbine,” Renew. Energy, vol. 145, pp. 893–902, 2020, doi: https://doi.org/10.1016/j.renene.2019.06.012.
  13. J. Sun et al., “Research on synergistic erosion by cavitation and sediment: A review,” Ultrason. Sonochem., vol. 95, p. 106399, 2023, doi: https://doi.org/10.1016/j.ultsonch.2023.106399.
  14. C. Cruzatty et al., “A Case Study: Sediment Erosion in Francis Turbines Operated at the San Francisco Hydropower Plant in Ecuador,” Energies, vol. 15, p. 8, 2021, doi: 10.3390/en15010008.
  15. A. Noon and M. H. Kim, “Sediment and Cavitation Erosion in Francis Turbines—Review of Latest Experimental and Numerical Techniques,” Energies, vol. 14, p. 1516, 2021, doi: 10.3390/en14061516.
  16. M. Toapanta, “Plan de control y aseguramiento de la calidad para la recuperación de un rodete de turbina Francis de una central hidroeléctrica,” Rev. Técnica “Energía,” vol. 15, no. 2, pp. 57–65, 2019, doi: 10.37116/revistaenergia.v15.n2.2019.377.
  17. C. D. Vieira et al., “Siltation processes and metal sediment profiles in a hydroelectric power plant reservoir in the Paraíba do Sul river Basin, Southeastern Brazil,” Environ. Earth Sci., vol. 81, no. 22, pp. 1–12, 2022, doi: 10.1007/s12665-022-10653-w.
  18. M. K. Padhy and R. P. Saini, “A review on silt erosion in hydro turbines,” Renew. Sustain. Energy Rev., vol. 12, no. 7, pp. 1974–1987, 2008, doi: https://doi.org/10.1016/j.rser.2007.01.025.
  19. S. Sharma, B. K. Gandhi, and L. Pandey, “Measurement and analysis of sediment erosion of a high head Francis turbine: A field study of Bhilangana-III hydropower plant, India,” Eng. Fail. Anal., vol. 122, p. 105249, 2021, doi: https://doi.org/10.1016/j.engfailanal.2021.105249.
  20. P. L. P. Mandeep Singh J. Banerjee and H. Tiwari, “Effect of silt erosion on Francis turbine: a case study of Maneri Bhali Stage-II, Uttarakhand, India,” ISH J. Hydraul. Eng., vol. 19, no. 1, pp. 1–10, 2013, doi: 10.1080/09715010.2012.738507.
  21. A. C. Yasir S. A. Ali Paolo Paron and Y. A. Mohamed, “Transboundary sediment transfer from source to sink using a mineralogical analysis. Case study: Roseires Reservoir, Blue Nile, Sudan,” Int. J. River Basin Manag., vol. 16, no. 4, pp. 477–491, 2018, doi: 10.1080/15715124.2017.1411919.
  22. H. Cüce, E. Kalipci, F. Ustaoğlu, M. A. Dereli, and A. Türkmen, “Integrated Spatial Distribution and Multivariate Statistical Analysis for Assessment of Ecotoxicological and Health Risks of Sediment Metal Contamination, Ömerli Dam (Istanbul, Turkey),” Water, Air, Soil Pollut., vol. 233, no. 6, p. 199, 2022, doi: 10.1007/s11270-022-05670-1.
  23. W. Quitiaquez, J. Estupinan-Campos, C. A. Isaza-Roldan, C. Nieto-Londono, P. Quitiaquez, and F. Toapanta-Ramos, “Numerical simulation of a collector/evaporator for direct-expansion solar-assisted heat pump,” 2020 Ieee Andescon, Andescon 2020, 2020, doi: 10.1109/ANDESCON50619.2020.9272139.
  24. P. W. Castro, “Sediment management at the water intake of hydropower plants,” Arab. J. Geosci., vol. 15, no. 12, p. 1118, 2022, doi: 10.1007/s12517-022-10405-x.
  25. W. Quitiaquez, I. Simbaña, C. A. Isaza-Roldán, P. Quitiaquez, C. Nieto-Londoño, and F. Toapanta-Ramos, “Revisión del estado del arte de sistemas DX-SAHP para la obtención de agua caliente sanitaria,” Enfoque UTE, vol. 11, no. 2, pp. 29–46, 2020, doi: 10.29019/enfoque.v11n2.565.
  26. A. Supithak, W. Wongsuwan, and L. Vongsarnpigoon, “Application of Minitab Statistical Software to Analysis of Students of the Faculty of Engineering , Analysis of Students the Faculty of Engineering , Thai-Nichi of of Technology Thai-Nichi,” J. Eng. Technol., vol. 4, no. 1, pp. 17–21, 2016.
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