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Study of power grid subnet partition based on graph neural network
Hongjun Wang1,2, Yanli Zou1,2, Tingli Qin1
1Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, Guangxi 541004, China.
Chaos (Woodbury, N.Y.)
|April 10, 2025
Summary
This study introduces a novel graph neural network method for power grid community segmentation, balancing network topology and power flow. This approach ensures each segment has reliable power generation, improving grid management.
Area of Science:
- Electrical Engineering
- Network Science
- Computational Science
Background:
- Power system reliability analysis is increasingly complex due to scale.
- Community structure is vital for power grid research but current methods neglect power balance.
- Existing segmentation methods require significant operational adjustments for independent subnetworks.
Purpose of the Study:
- To propose an improved community segmentation method for power networks.
- To integrate network topology with power balance considerations.
- To enhance the operational feasibility of segmented power grids.
Main Methods:
- Utilizing graph neural networks (GNNs) for community segmentation.
- Incorporating node features like degree, betweenness, and power value.
- Optimizing the K-means algorithm with generator nodes as cluster centers.
Main Results:
- The proposed GNN-based method effectively segments power networks.
- The approach ensures power balance and generator presence within each community.
- Experimental validation on IEEE test systems demonstrates superior performance.
Conclusions:
- The integrated approach offers a more robust power grid segmentation.
- This method facilitates more efficient and reliable operation of independent subnetworks.
- The GNN-based strategy advances power system community analysis and management.
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