Abstract
The smart grid is an advanced power system concept that integrates electricity and communication within system networks. Its primary goal is to provide real-time information to producers, operators, and consumers. Efficiently routing energy to different consumer domains, including households, organizations, industries, and smart cities, is of utmost importance due to the increasing demand for dynamic energy supply. To address the need for a stable smart grid system that can meet these demands, we propose a method in this study to detect the stability of a smart grid. For this purpose, we employ fuzzy supervised machine learning, which has proven to be effective in handling uncertain data. Using a dataset containing 60,000 smart grid observations, we evaluate our approach and present compelling results, demonstrating the efficiency of the fuzzy machine learning model in detecting different states of the smart grid.
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Acknowledgements
This work has been partially supported by EU DUCA, EU CyberSecPro, EU E-CORRIDOR, PTR 22-24 P2.01 (Cybersecurity), and SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the EU – NextGenerationEU projects.
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Martinelli, F., Mercaldo, F., Santone, A. (2024). Fuzzy Machine Learning for Smart Grid Instability Detection. In: Sipola, T., Alatalo, J., Wolfmayr, M., Kokkonen, T. (eds) Artificial Intelligence for Security. Springer, Cham. https://doi.org/10.1007/978-3-031-57452-8_10
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