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Sustainability
Volume 14
Issue 15
10.3390/su14159113
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Article

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

by
José Manuel Gámez Medina
1,
Jorge de la Torre y Ramos
2,*,
Francisco Eneldo López Monteagudo
2,
Leticia del Carmen Ríos Rodríguez
3,
Diego Esparza
2,
Jesús Manuel Rivas
2,
Leonel Ruvalcaba Arredondo
3 and
Alejandra Ariadna Romero Moyano
3
1
Unidad Académica de Ingeniería I, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico
2
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico
3
Unidad Académica de Docencia Superior, Universidad Autónoma de Zacatecas, Zacatecas P.C. 98000, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(15), 9113; https://doi.org/10.3390/su14159113
Submission received: 8 June 2022 / Revised: 13 July 2022 / Accepted: 19 July 2022 / Published: 25 July 2022
(This article belongs to the Special Issue Energy Efficiency in Power Lines)
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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 power factor; prediction; three phase systems; machine learning

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MDPI and ACS Style

Gámez Medina, J.M.; de la Torre y Ramos, J.; López Monteagudo, F.E.; Ríos Rodríguez, L.d.C.; Esparza, D.; Rivas, J.M.; Ruvalcaba Arredondo, L.; Romero Moyano, A.A. Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning. Sustainability 2022, 14, 9113. https://doi.org/10.3390/su14159113

AMA Style

Gámez Medina JM, de la Torre y Ramos J, López Monteagudo FE, Ríos Rodríguez LdC, Esparza D, Rivas JM, Ruvalcaba Arredondo L, Romero Moyano AA. Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning. Sustainability. 2022; 14(15):9113. https://doi.org/10.3390/su14159113

Chicago/Turabian Style

Gámez Medina, José Manuel, 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. 2022. "Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning" Sustainability 14, no. 15: 9113. https://doi.org/10.3390/su14159113

APA Style

Gámez Medina, J. M., de la Torre y Ramos, J., López Monteagudo, F. E., Ríos Rodríguez, L. d. C., Esparza, D., Rivas, J. M., Ruvalcaba Arredondo, L., & Romero Moyano, A. A. (2022). Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning. Sustainability, 14(15), 9113. https://doi.org/10.3390/su14159113

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