The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Power System Fault Diagnosis and Prediction System Based on Graph Neural Network
Abstract
The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the operation and maintenance of the power system. This article presents an approach employing graph neural network (GNN) to enhance the precision and efficiency of power system fault diagnosis and prediction. The system's efficacy lies in its ability to capture the intricate interconnections and dynamic variations within the power system by conceptualizing it as a graph structure and harnessing the capabilities of GNN. In this study, the authors introduce a substitution for the pooling layer with a convolution operation. A central role is played by the global average pooling layer, connecting the convolution layer and the fully connected layer. The fully connected layer carries out nonlinear computations, ultimately providing the classification at the top-level output layer. In experiments and tests, we verified the performance of the system.
Related Content
|
Tianlong Wang, Chaoyang Wang, Zhiqiang Liu, Shuai Ma, Huibo Yan.
© 2024.
15 pages.
|
|
Xudong Cao, Chenchen Chen, Lejia Zhang, Li Pan.
© 2024.
25 pages.
|
|
Shengfeng Xie, Jingwei Li.
© 2024.
20 pages.
|
|
Xiaoyuan Wang, Hongfei Wang, Jianping Wang, Jiajia Wang.
© 2024.
24 pages.
|
|
Jiao Hao, Zongbao Zhang, Yihan Ping.
© 2024.
14 pages.
|
|
Qinmei Wang.
© 2024.
13 pages.
|
|
Wenzhen Mai, Mohamud Saeed Ambashe, Chukwuka Christian Ohueri.
© 2024.
18 pages.
|
|
|