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

页码：215 / 222

页数：7

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