Peifa Shan

China Southern Power Grid (China)

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Rongbo Pan

China Southern Power Grid (China)

Yaopeng Zhao

Chinese Academy of Sciences

Yangyang Li

Nanjing University of Aeronautics and Astronautics

Yaopeng Zhao

China Southern Power Grid (China)

Min Lei

China Southern Power Grid (China)

Yangyang Li

University of Science and Technology of China

Min Lei

Northwest University

Lei Han

China Southern Power Grid (China)

Yangyang Li

Xinyang Normal University

Lei Han

Beijing Institute of Radio Metrology and Measurement

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China Southern Power Grid (China)

Beijing University of Posts and Telecommunications

Hunan University

Dalian University

Nanjing University of Aeronautics and Astronautics

Hebei University of Technology

Henan University of Science and Technology

Aero Engine Corporation of China (China)

Shenyang Jianzhu University

Zhejiang University of Technology

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Fault location estimation of power grid using graph attention networks

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) (2024)

Peifa Shan Lei Han Min Lei Rongbo Pan Yaopeng Zhao

The reliability of power grids is of paramount importance to modern infrastructure. Precise fault location estimation is crucial for the efficient operation and rapid recovery of electrical networks following outages. This paper presents a novel approach using Graph Attention Networks (GAT) to improve fault location estimation within power grids. Leveraging two years of real-world data from a power grid's monitoring system, encompassing 200 fault instances, our model demonstrates a significant advancement over traditional methods. The GAT model capitalizes on an attention-driven mechanism, providing a dynamic and focused analysis of the grid's topological data, which enhances the accuracy of fault detection. Comparative experiments show that GAT model outperforms benchmark algorithms, Graph Convolutional Networks (GCN), and Graph Neural Networks (GNN), with lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The results highlight the GAT's potential as a robust and reliable tool for fault diagnosis in power grids, promising substantial improvements in operational resilience and maintenance efficiency.

Power grid

10.1117/12.3026821

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Fault Detection of Power Grid Using Graph Convolutional Networks

Min Lei Rongbo Pan Lei Han Peifa Shan Yaopeng Zhao

Power grid

10.1145/3674225.3674273

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The Application of Artificial Intelligence Technology in the Fault Diagnosis of Assets in Power Grid

Atlantis highlights in social sciences, education and humanities/Atlantis Highlights in Social Sciences, Education and Humanities (2023)

Rongbo Pan Min Lei Mingjie Zhang Peifa Shan Yaopeng Zhao

Power grid

10.2991/978-94-6463-230-9_155

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