Prediction of generating machine shutdowns at a hydroelectric power station by data mining

被引：0
|
作者：
González H.A. [1 ]
Piedrahita J.D. [1 ]
Castrillón O.D. [1 ]
机构：
[1] Universidad Nacional de Colombia, Facultad de Ingeniería y Arquitectura, Departamento de Ingeniería Industrial, Campus La Nubia Bloque Q piso 2, Manizales
来源：
Informacion Tecnologica | 2020年 / 21卷 / 05期
关键词：
Bayesian; Control sheets; Data mining; Generating machines; Hydroelectric power station;
D O I：
10.4067/S0718-07642020000500215
中图分类号：
学科分类号：
摘要：
A methodology is developed to predict shutdowns of generating machines in a hydroelectric power station by using the platform Weka and the algorithm J48. A file was built with 300 real data registrations and 11 variables consisting of a single dependent variable (machine trip) and ten independent variables: Temperature, water flow, regulatory pressure, pipeline pressure, flow rate of the reservoir level, generated load, frequency, oil temperature, and climate. Using XRealStats tools, a Pearson correlation analysis was performed between each of the independent variables and the dependent variable. The results showed that it is possible to predict machine trips with a success rate of 94%. It is concluded that the classification tree generated in this research predicts future machine trips with over 94% accuracy. © 2020 Centro de Informacion Tecnologica. All rights reserved.
引用
收藏
页码：215 / 222
页数：7
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