Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant
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...
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| Format: | Article |
| Language: | English |
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Operador Nacional de Electricidad – CENACE
2025-01-01
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| Series: | Revista Técnica Energía |
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| Online Access: | https://revistaenergia.cenace.gob.ec/index.php/cenace/article/view/691 |
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| author | Kleber Zhañay Cristian Leiva Erika Pilataxi William Quitiaquez |
| author_facet | Kleber Zhañay Cristian Leiva Erika Pilataxi William Quitiaquez |
| author_sort | Kleber Zhañay |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-c809c1d0e2bd44578f2cf7383d300036 |
| institution | Kabale University |
| issn | 1390-5074 2602-8492 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Operador Nacional de Electricidad – CENACE |
| record_format | Article |
| series | Revista Técnica Energía |
| spelling | doaj-art-c809c1d0e2bd44578f2cf7383d3000362025-02-02T14:34:37ZengOperador Nacional de Electricidad – CENACERevista Técnica Energía1390-50742602-84922025-01-0121210.37116/revistaenergia.v21.n2.2025.691Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant Kleber ZhañayCristian Leivahttps://orcid.org/0000-0002-8255-1337Erika Pilataxi0https://orcid.org/0009-0009-2633-0407William Quitiaquezhttps://orcid.org/0000-0001-9430-2082Universidad Politécnica Salesiana, 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. https://revistaenergia.cenace.gob.ec/index.php/cenace/article/view/691Hydroelectric power plantsedimentsturbinemaintenance |
| spellingShingle | Kleber Zhañay Cristian Leiva Erika Pilataxi William Quitiaquez Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant Revista Técnica Energía Hydroelectric power plant sediments turbine maintenance |
| title | Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant |
| title_full | Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant |
| title_fullStr | Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant |
| title_full_unstemmed | Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant |
| title_short | Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant |
| title_sort | wear sediment quantity correlation model for preventive maintenance scheduling of a hydroelectric power plant |
| topic | Hydroelectric power plant sediments turbine maintenance |
| url | https://revistaenergia.cenace.gob.ec/index.php/cenace/article/view/691 |
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