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Item type:Publication, Wear - Sediment Quantity Correlation Model for Preventive Maintenance Scheduling of a Hydroelectric Power Plant(2025-01-30)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.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gully erosion assessment in mountainous regions: a RUSLE-based methodology for the Central Peruvian Andes(Taylor and Francis Ltd., 2026)Soil erosion is a significant global threat, with gully erosion forming deep hillside channels that substantially contribute to sediment yield. While erosion is often assessed using models like the Revised Universal Soil Loss Equation (RUSLE), its limitation to only consider surface erosion, avoiding severe erosion processes, led us to develop RUSLEad, an adapted version for gully erosion. The method integrates the Topographic Wetness Index (TWI) and a region-specific sediment delivery ratio in its formulation and includes generalized likelihood uncertainty estimation for model validation. Our pilot study is a basin of 13 km2 located in the Central Peruvian Andes. There, we collected detailed topographic and soil data. Chosen for its cost-effectiveness and data accessibility, RUSLEad produced erosion maps and average sediment yield estimates, and identified erosion hotspots. This method, applicable to similar terrain elsewhere, supports Peru’s Climate Change Strategy 2050 by informing gully control and contributing to prioritizing potential remediation efforts.