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Fault Detection of Power Grid Using Graph Convolutional Networks

Published: 31 July 2024 Publication History

Abstract

Power grids, the linchpin of modern electrical infrastructure, necessitate advanced monitoring systems to ensure operational stability and safety. This paper presents an in-depth investigation into the application of Graph Convolutional Networks (GCN) for fault detection within power grids. Utilizing authentic data collected over two years from a real-world power grid, the research benchmarks the performance of GCN against established algorithms: CNN, LSTM, and ANN. Preliminary findings highlight the unmatched accuracy of GCN, surpassing 91%, emphasizing their proficiency in processing graph-structured data. While CNN and LSTM showcase respectable results, their inherent design indicates certain limitations for grid fault detection. The overarching conclusion suggests a promising avenue for GCN in enhancing power grid monitoring, potentially revolutionizing the methods by which we maintain and secure critical electrical infrastructures.

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  1. Fault Detection of Power Grid Using Graph Convolutional Networks

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 31 July 2024

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