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Artificial Intelligence-Assisted Edge Computing for Wide Area Monitoring

Published

Author(s)

Bin Hu, Hamid Gharavi

Abstract

The massive digital information generated in conjunction with the ever-increasing phasor measurement data in the power grid has led to a tremendous constraint on the analysis and timely processing of real-time data. Under these conditions, leveraging Artificial Intelligence (AI) can play a crucial role in assisting more efficient data processing and analysis. In this paper an AI-assisted power grid event classification method is proposed, which aims at improving the overall power grid system performance. Furthermore, an edge cloud sharing scheme is introduced for a large-scale power grid system. To balance the load and reduce the maximum processing time, a multiple edge cloud node-based scheme is developed. The simulation results verify that the proposed AI-assisted event classification method, together with the edge cloud sharing scheme, can significantly improve the overall performance of the system.
Citation
IEEE Open Journal of the Communications Society
Volume
4

Keywords

AlexNet, Classification and Identification, CNN, Edge Cloud, GRU, LeNet, LSTM, Machine Learning, Smart Grid, SVM

Citation

Hu, B. and Gharavi, H. (2023), Artificial Intelligence-Assisted Edge Computing for Wide Area Monitoring, IEEE Open Journal of the Communications Society, [online], https://doi.org/10.1109/OJCOMS.2023.3292050, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956267 (Accessed May 25, 2024)

Issues

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Created July 7, 2023, Updated August 7, 2023