Li, Yiyan, Zhou, Zhenghao, Ping, Jian, Xu, Xiaoyuan, Yan, Zheng and Wu, Jianzhong  ORCID: https://orcid.org/0000-0001-7928-3602
2026.
A two-stage AI-powered motif mining method for efficient power system topological analysis.
Applied Energy
416
, 127992.
10.1016/j.apenergy.2026.127992
|
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power system is extremely computationally challenging and even infeasible, which undermines the value of motif analysis in practice. In this paper, we introduce a two-stage AI-powered motif mining method to enable efficient and wide-range motif analysis in power systems. In the first stage, a representation learning method with specially designed network structure and loss function is proposed to achieve ordered embedding for the power system topology, simplifying the subgraph isomorphic problem into a vector comparison problem. In the second stage, under the guidance of the ordered embedding space, a greedy-search-based motif growing algorithm is introduced to quickly obtain the motifs without traversal searching. A case study based on a power system database including 61 circuit models demonstrates the effectiveness of the proposed method.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Additional Information: | RRS policy applied |
| Publisher: | Elsevier |
| ISSN: | 0306-2619 |
| Date of First Compliant Deposit: | 10 June 2026 |
| Date of Acceptance: | 26 April 2026 |
| Last Modified: | 10 Jun 2026 09:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/186909 |
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions