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
相关论文
共 50 条
  • [21] Reliability Evaluation of Flood Releasing Structures Power Supply of Hydroelectric Power Station by Fault Tree Analysis
    Li, Zhenjie
    Yuan, Yue
    Wu, Bowen
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [22] Data Mining and Machine Learning Based Approach to Inventory Prediction-A Case Study
    Thiruverahan, Nivetha
    Subramanian, Narayanaswamy
    2015 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ARTIFICIAL INTELLIGENCE (CAAI 2015), 2015, : 195 - 202
  • [23] Machine learning-data mining integrated approach for premature ventricular contraction prediction
    Qurat-ul-ain Mastoi
    Muhammad Suleman Memon
    Abdullah Lakhan
    Mazin Abed Mohammed
    Mumtaz Qabulio
    Fadi Al-Turjman
    Karrar Hameed Abdulkareem
    Neural Computing and Applications, 2021, 33 : 11703 - 11719
  • [24] Machine learning and artificial neural network for data mining classification and prediction of brain diseases
    Dawood A.S.
    International Journal of Reasoning-based Intelligent Systems, 2023, 15 (3-4) : 313 - 322
  • [25] Machine learning-data mining integrated approach for premature ventricular contraction prediction
    Mastoi, Qurat-ul-ain
    Memon, Muhammad Suleman
    Lakhan, Abdullah
    Mohammed, Mazin Abed
    Qabulio, Mumtaz
    Al-Turjman, Fadi
    Abdulkareem, Karrar Hameed
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (18) : 11703 - 11719
  • [26] Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach
    Zheng, Haiyang
    Kusiak, Andrew
    JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME, 2009, 131 (03): : 0310111 - 0310118
  • [27] A Comprehensive Prototype Design of Hydroelectric Power Station for Floating-Cages Energy Resources
    Fakhrudin, Z.
    Irawan, Dedi
    Irfan, Zul
    Islami, Nur
    UNIVERSITAS RIAU INTERNATIONAL CONFERENCE ON SCIENCE AND ENVIRONMENT 2020 (URICSE-2020), 2020, 1655
  • [28] Evolution of the Salto of Jandula project. Process of integration of the hydroelectric power station in the dam
    Carbajal-Ballell, N.
    INFORMES DE LA CONSTRUCCION, 2019, 71 (554)
  • [29] Evaluating data mining procedures: techniques for generating artificial data sets
    Scott, PD
    Wilkins, E
    INFORMATION AND SOFTWARE TECHNOLOGY, 1999, 41 (09) : 579 - 587
  • [30] Machine learning and data mining in manufacturing
    Dogan, Alican
    Birant, Derya
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 166
12345