Local Volt-Var Control Applied in an Islanded Microgrid Using Supervised Learning Techniques

Authors

Keywords:

microgrids, power quality, smart inverter, volt-var control, artificial intelligence, machine learning

Abstract

The electrical sector pursuit of technical and ecological alternatives makes it possible to integrate and cooperatively optimize dispersed energy resources, enhancing the stability, dependability, and resilience of contemporary energy systems. Microgrids and artificial intelligence are two ideas that could be included into contemporary power grids in an effort to lower costs and pollution emissions. This work proposes a new energy control and management strategy based on smart devices in this context. It explores machine-learning techniques for implementing supervised learning algorithms to perform automatic volt-var control adjustments and mitigate voltage fluctuations at the point of common coupling using smart inverters. The techniques explored and compared in this study include multilayer perceptron, SVM, and random forest. The results were consistent, with average accuracies above 90%, indicating the relevance of the analyzed models for this application. Thus, this research seeks to improve power quality in islanded microgrids with high penetration of distributed generation and explore the potential of artificial intelligence in decision-making processes.

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Author Biographies

Diego D. Domingues, Universidade Católica de Pelotas

Diego D. Domingues is a Master's degree in Electronic and Computer Engineering at UCPel (2024), Pelotas, Brazil. Bachelor's degree in Electrical Engineering from UCPel (2018) and Technician in Electrotechnics from Instituto Federal Sul-Riograndense – IFSul (2009). He has experience in the areas of Electrical and Industrial Installations, Information Technology, Telecommunications Systems, Protection and Control of Power Systems, Automation and Renewable Energy Sources. His research interests are in Machine Learning, IoT, Microgrids, Power Systems, Electrical and Electronic Circuits.

Sérgio J. M. Almeida, Universidade Católica de Pelotas

Sérgio J. M. Almeida. (M’11) received the B.E.E. degree from the Federal University of Pernambuco (UFPE), Pernambuco, Brazil, in 1988, the M.Sc. degree in electrical engineering from Federal University of Paraíba (UFPB), Paraíba, Brazil, in 1991, and the Ph.D. degree in electrical engineering from the Federal University of Santa Catarina (UFSC), Florianópolis, Brazil, in 2004. He was a Postdoctoral in the Department of Electrical and Electronic Engineering at the Federal University of Santa Catarina, Brazil, from 2009 to 2010. Currently, he is a Professor of electrical engineering and computer science of the Catholic University of Pelotas, Pelotas, Brazil. His research interests are in digital signal processing, including statistical signal processing, adaptive algorithm, hyperspectral image processing, and dedicated hardware for signal processing.

Eduardo A. C. Costa, Universidade Católica de Pelotas

Eduardo A. C. Costa. received the five-year engineering degree in Electrical Engineering from the University of Pernambuco, Recife, Brazil, in 1988, the M.Sc. degree in electrical engineering from the Federal University of Paraíba, Campina Grande, Paraíba, Brazil, in 1991, and the Ph.D. degree in computer science from the Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil, in 2002.  Part of his doctoral work was developed at the Instituto de Engenharia de Sistemas Computadores (INESC-ID), Lisbon, Portugal. He is currently a Full Professor at the Universidade Católica de Pelotas (UCPel), Pelotas, Brazil. He is co-founder and coordinator of the Graduate Program on Electronic Engineering and Computing at UCPel. His research interests are VLSI architectures and low-power design.

References

J. P. M. Abuabud and P. H. A. Barra, “Estudo sistemático sobre microrredes e redes elétricas inteligentes,” Brazilian Journal of Development, vol. 6, no. 9, pp. 65711–65727, 2020. DOI: 10.34117/bjdv6n9-122.

N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “An overview of ongoing research, development, and demonstration projects,” IEEE power & Energy magazine, vol. 5, no. 4, pp. 79–94, 2007. DOI: 10.1109/MPAE.2007.376583.

R. M. Soares and M. E. de Oliveira, “Microrredes: o conceito através da história, incentivos eo mercado brasileiro,” Simpósio Brasileiro de Sistemas Elétricos-SBSE, vol. 2, no. 1, 2022. DOI: 10.20906/sbse.v2i1.2988.

E. B. d. Oliveira, R. M. Martins, G. Marchesan, J. Parizzi, and C. Lazaro, “Análise de utilização de controle volt-var em sistemas com grande penetração de geração fotovoltaica,” in 15th Seminar on Power Electronics and Control (SEPOC 2023), Brasil, 2023. DOI: 10.53316/sepoc2023.035.

L. C. R. Junior, “Inversores inteligentes em sistemas fotovoltaicos para controle integrado de funções utilizando o opendss,” Master’s thesis, Universidade Federal de Itajubá (UNIFEI), 2018. Disponível em: https: //repositorio.unifei.edu.br/xmlui/handle/123456789/1757.

L. P. Carlette, “Análise do impacto de inversores inteligentes aplicados a redes de baixa tensão.” Disponível em: https://pantheon.ufrj.br/handle/11422/9796, 2019.

S. Sreekumar, D. S. Kumar, and J. Savier, “A case study on self healing of smart grid with islanding and inverter volt–var function,” IEEE Transactions on Industry Applications, vol. 56, no. 5, pp. 5408–5416, 2020. DOI: 10.1109/TIA.2020.3011664.

A. P. C. de Mello, D. P. Bernadon, L. L. Pfitscher, and W. S. Hokama, “Controle volt/var coordenado para operação de sistemas de distribuição inteligentes,” in Congresso Brasileiro de Automática - CBA, vol. 1, 2019. DOI: doi.org/10.20906/CBA2022/44.

H. A. Florez, G. P. López, E. M. Carreño-Franco, J. M. López-Lezama, and N. Muñoz-Galeano, “Application of intelligent systems in voltvar centralized control in modern distribution systems of electrical energy,” Electronics, vol. 11, no. 3, p. 446, 2022. DOI: 10.3390/electronics11030446.

A. F. M. Jaramillo, J. Lopez-Lorente, D. M. Laverty, P. V. Brogan, S. H. H. Velasquez, J. Martinez-Del-Rincón, and A. M. Foley, “Distributed energy resources electric profile identification in low voltage networks using supervised machine learning techniques,” IEEE Access, vol. 11, pp. 19469–19486, 2023. DOI: 10.1109/ACCESS.2023.3247977.

S. Suganthi, A. Vinayagam, V. Veerasamy, A. Deepa, M. Abouhawwash, and M. Thirumeni, “Detection and classification of multiple power quality disturbances in microgrid network using probabilistic based intelligent classifier,” Sustainable Energy Technologies and Assessments, vol. 47, p. 101470, 2021. DOI: 10.1016/j.seta.2021.101470.

L. Zjavka, “Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing nwp-data,” Energy Strategy Reviews, vol. 47, p. 101076, 2023. DOI: 10.1016/j.esr.2023.101076.

P. I. d. N. Barbalho, V. Lacerda, R. Fernandes, and D. V. Coury, “Deep reinforcement learning-based secondary control for microgrids in islanded mode,” Electric Power Systems Research, vol. 212, p. 108315, 2022. DOI: 10.1016/j.epsr.2022.108315.

L. de Carvalho Guerra, “Estratégia de gerenciamento de energia de microrrede baseada em algoritmo de otimização global,” Master’s thesis, Centro Universitário SENAI CIMATEC, Salvador, BA, 2023. Disponível em: http://repositoriosenaiba.fieb.org.br/handle/fieb/1846.

L. G. R. Bernardino, “Estimador seletivo do conteúdo harmônico de tensão e corrente baseado em rede neural profunda,” Master’s thesis, Universidade Federal de São Carlos, São Carlos, SP, 2022. Disponível em: https://repositorio.ufscar.br/handle/ufscar/15941.

G. Lopes Filho, R. A. P. Franco, F. H. T. Vieira, and C. A. G. Medeiros, “Algoritmo de controle de potência reativa para adequação de valores de tensão e redução de perdas em sistemas de distribuição,” Eletrônica de Potência, vol. 26, no. 3, pp. 290–301, 2021. DOI: 10.18618/REP.2021.3.0014.

G. P. L. Sepúlveda, Aplicação de inteligência computacional na resolução de problemas de sistemas elétricos de potência. PhD thesis, Universidade Estadual Paulista (Unesp), 2017. Disponível em: https: //repositorio.unesp.br/handle/11449/151837.

T. F. Santos and V. H. Ferreira, “Voltage control in microgrids with minimum adjustment in distributed generation units,” in 2018 Simposio Brasileiro de Sistemas Eletricos (SBSE), pp. 1–6, 2018. DOI: 10.1109/SBSE.2018.8395876.

S. Gupta, V. Kekatos, and S. Chatzivasileiadis, “Optimal design of volt/var control rules of inverters using deep learning,” IEEE Transactions on Smart Grid, vol. 15, no. 5, pp. 4731–4743, 2024. DOI: 10.1109/TSG.2024.3381984.

R. C. Marques, “Detecção de desequilíbrio de tensão em microrredes utilizando redes neurais perceptron,” Master’s thesis, Universidade Estadual Paulista (UNESP), 2022. Disponível em: https://repositorio.unesp.br/handle/11449/234109.

ANEEL, “Regras e procedimentos de distribuição (prodist).” Acessível online: https://www.gov.br/aneel/pt-br/centrais-deconteudos/procedimentos-regulatorios/prodist, 2022. Acesso em: 15-08-2024.

F. de Carvalho Castro, “Distorção harmônica de corrente produzida por inversores fotovoltaicos conectados à rede,” Master’s thesis, Universidade Federal de Goiás (UFG), 2019. Disponível em: http://repositorio.bc.ufg.br/tede/handle/tede/10152.

R. M. Martins, “Impacto do controle de tensão e potência reativa (volt-var) em sistemas de distribuição com alta penetração de geração distribuída fotovoltaica,” Master’s thesis, Universidade Federal de Santa Maria (UFSM), 2023. Disponível em: https://repositorio.ufsm.br/handle/1/28696.

D. Van Zandt, “Mitigation methods to increase feeder hosting capacity 3002013382; epri report,” Electric Power Research Institute (EPRI): Palo Alto, CA, USA, 2018.

A. Géron, Mãos à Obra: Aprendizado de Máquina com Scikit-Learn & TensorFlow. Alta Books, 2019.

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Published

2025-06-12

How to Cite

Dias Domingues, D., José Melo Almeida, S., & Antonio César Costa, E. . (2025). Local Volt-Var Control Applied in an Islanded Microgrid Using Supervised Learning Techniques. IEEE Latin America Transactions, 23(7), 600–608. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9469

Issue

Vol. 23 No. 7 (2025): Ordinary Issue

Section

Electric Energy