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Artificial Intelligence-Assisted Edge Computing for Wide Area Monitoring



Bin Hu, Hamid Gharavi


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.
IEEE Open Journal of the Communications Society


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


Hu, B. and Gharavi, H. (2023), Artificial Intelligence-Assisted Edge Computing for Wide Area Monitoring, IEEE Open Journal of the Communications Society, [online],, (Accessed May 25, 2024)


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Created July 7, 2023, Updated August 7, 2023