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Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning

Author

Listed:
  • José Manuel Gámez Medina

    (Unidad Académica de Ingeniería I, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Jorge de la Torre y Ramos

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Francisco Eneldo López Monteagudo

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Leticia del Carmen Ríos Rodríguez

    (Unidad Académica de Docencia Superior, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Diego Esparza

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Jesús Manuel Rivas

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Leonel Ruvalcaba Arredondo

    (Unidad Académica de Docencia Superior, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

  • Alejandra Ariadna Romero Moyano

    (Unidad Académica de Docencia Superior, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico)

Abstract

The power factor in electrical power systems is of paramount importance because of the influence on the economic cost of energy consumption as well as the power quality requested by the grid. Low power factor affects both electrical consumers and suppliers due to an increase in current requirements for the installation, bigger sizing of industrial equipment, bigger conductor wiring that can sustain higher currents, and additional voltage regulators for the equipment. In this work, we present a technique for predicting power factor variations in three phase electrical power systems, using machine learning algorithms. The proposed model was developed and tested in medium voltage installations and was found to be fairly accurate thus representing a cost reduced approach for power quality monitoring. The model can be modified to predict the variation of the power factor, taking into account removable energy sources connected to the grid. This new way of analyzing the behavior of the power factor through prediction has the potential to facilitate decision-making by customers, reduce maintenance costs, reduce the probability of injecting disturbances into the network, and above all affords a reliable model of behavior without the need for real-time monitoring, which represents a potential cost reduction for the consumer.

Suggested Citation

  • José Manuel Gámez Medina & Jorge de la Torre y Ramos & Francisco Eneldo López Monteagudo & Leticia del Carmen Ríos Rodríguez & Diego Esparza & Jesús Manuel Rivas & Leonel Ruvalcaba Arredondo & Alejand, 2022. "Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9113-:d:871141
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    References listed on IDEAS

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    1. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
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    1. Manuela Panoiu & Caius Panoiu & Sergiu Mezinescu & Gabriel Militaru & Ioan Baciu, 2023. "Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply," Mathematics, MDPI, vol. 11(6), pages 1-20, March.

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