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

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 and Alejandra Ariadna Romero Moyano
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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

Sustainability, 2022, vol. 14, issue 15, 1-14

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.

Keywords: power factor; prediction; three phase systems; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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