INTELLIGENT MONITORING OF ELECTRICAL NETWORKS: TECHNOLOGICAL FOUNDATIONS, APPLICATIONS, AND CHALLENGES IN THE CONTEXT OF SMART GRIDS
DOI:
https://doi.org/10.63330/armv2n2-004Keywords:
Intelligent monitoring, Electrical networks, Smart grids, Internet of Things, Artificial IntelligenceAbstract
Intelligent monitoring of electrical networks constitutes a central element in the modernization of contemporary energy infrastructure, particularly in the context of the digitalization of generation, transmission, and distribution systems. The growing operational complexity resulting from the integration of intermittent renewable sources, decentralization of generation, and expansion of consumption demands mechanisms capable of collecting, processing, and interpreting data in real time. In this scenario, technologies such as advanced sensors, smart meters, enhanced SCADA systems, the Internet of Things (IoT), and Artificial Intelligence (AI) algorithms converge to form monitoring architectures aimed at increasing the reliability, efficiency, and resilience of electrical networks. This study critically analyzes the technical foundations, practical applications, and challenges associated with intelligent network monitoring, discussing its contribution to early fault detection, predictive maintenance, demand management, and systemic stability. It is based on the premise that real-time monitoring capability does not represent a mere incremental innovation but rather a structural reconfiguration of the operational model of electrical networks, in which data are converted into automated decisions. It is argued that the incorporation of distributed sensors and advanced analytical platforms reduces technical losses, optimizes power flow, and expands the capacity to respond to critical events, although it introduces challenges related to cybersecurity, technological interoperability, and data governance. It is concluded that intelligent monitoring constitutes a strategic pillar of smart grids, requiring integration between electrical engineering, data science, and technological regulation to ensure energy sustainability and reliability.
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References
ABB. Digital Substation Monitoring Report. Zürich: ABB, 2022.
ATZORI, Luigi; IERA, Antonio; MORABITO, Giacomo. The Internet of Things: A survey. Computer Networks, v. 54, n. 15, p. 2787-2805, 2010.
BARDIN, Laurence. Análise de conteúdo. São Paulo: Edições 70, 2011.
DUNN, Bruce; KAMATH, Haresh; TARASCON, Jean-Marie. Electrical energy storage for the grid. Science, v. 334, n. 6058, p. 928-935, 2011.
FANG, Xi et al. Smart grid – the new and improved power grid: A survey. IEEE Communications Surveys & Tutorials, v. 14, n. 4, p. 944-980, 2012.
GIL, Antonio Carlos. Métodos e técnicas de pesquisa social. 7. ed. São Paulo: Atlas, 2019.
GOODFELLOW, Ian; BENGIO, Yoshua; COURVILLE, Aaron. Deep Learning. Cambridge: MIT Press, 2016.
GUNGOR, Vehbi C. et al. Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, v. 7, n. 4, p. 529-539, 2011.
GUNGOR, Vehbi C.; HANCKE, Gerhard P. Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics, v. 56, n. 10, p. 4258-4265, 2009.
HONG, Tao; FAN, Shu. Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting, v. 32, n. 3, p. 914-938, 2016.
IEA. Digitalization and Energy. Paris: International Energy Agency, 2017.
IEA. World Energy Outlook 2023. Paris: International Energy Agency, 2023.
IEEE. IEEE Guide for Monitoring and Control of Electric Power Systems. New York: IEEE, 2021.
KONG, Weicong et al. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, v. 10, n. 1, p. 841-851, 2017.
LOPES, João A. P.; SOARES, Filipe J.; ALMEIDA, Pedro M. Integration of electric vehicles in the electric power system. Proceedings of the IEEE, v. 99, n. 1, p. 168-183, 2011.
MOMOH, James. Smart Grid: Fundamentals of Design and Analysis. Hoboken: Wiley, 2012.
NIST. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0. Gaithersburg: NIST, 2014.
OECD. Energy Infrastructure Digitalization Report. Paris: OECD, 2023.
PAIXÃO, J. L. Inovação tecnológica em redes elétricas: avanços, desafios e perspectivas na era das smart grids e da descentralização energética. Revista Tópicos, v. 4, p. 1-23, 2026a.
PAIXÃO, J. L. Transformação digital no setor elétrico: o papel central das redes inteligentes. Revista Tópicos, v. 4, p. 1-19, 2026b.
PAIXÃO, J. L. Energia elétrica e transformação tecnológica: digitalização, automação e inovação nos sistemas elétricos contemporâneos. Revista Tópicos, v. 4, p. 1-21, 2026c.
PAIXÃO, J. L. Internet das Coisas (IoT): conceitos, desafios e aplicações no contexto das redes elétricas inteligentes. In: Inovações Multidisciplinares na Engenharia. Curitiba: Aurum Editora, 2025. p. 1-11.
PAIXÃO, J. L.; ABAIDE, A. R. Redes elétricas inteligentes: smart grids. In: Caminhos da Pesquisa Multidisciplinar. Curitiba: Aurum Editora, 2025a. p. 25-35.
PAIXÃO, J. L.; ABAIDE, A. R. Sistemas elétricos resilientes e segurança energética: uma análise integrada a partir de pesquisas em microrredes, veículos elétricos e fontes renováveis. Revista Tópicos, v. 4, p. 1-22, 2026.
PALATELLA, Maria Rita et al. Internet of Things in the 5G era: Enablers, architecture, and business models. IEEE Journal on Selected Areas in Communications, v. 34, n. 3, p. 510-527, 2016.
PHADKE, Arun G.; THORP, James S. Synchronized Phasor Measurements and Their Applications. 2. ed. New York: Springer, 2017.
RUSSELL, Stuart; NORVIG, Peter. Artificial Intelligence: A Modern Approach. 4. ed. Hoboken: Pearson, 2020.
SUN, Hao et al. Reinforcement learning for power systems optimization: A review. Applied Energy, v. 270, 2020.
VOYANT, Cyril et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, v. 105, p. 569-582, 2017.
WORLD ECONOMIC FORUM. The Future of Electricity Infrastructure. Geneva: WEF, 2022.
YAN, Ying et al. A survey on cyber security for smart grid communications. IEEE Communications Surveys & Tutorials, v. 14, n. 4, p. 998-1010, 2012.
ZHANG, Yan et al. Home energy management systems: A survey of architectures and technologies. IEEE Communications Surveys & Tutorials, v. 15, n. 4, p. 2033-2055, 2013.
ZHANG, Zhe et al. Deep learning-based fault diagnosis in power systems: A comprehensive review. IEEE Transactions on Power Systems, v. 34, n. 6, p. 4396-4408, 2019.
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