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A two-stage AI-powered motif mining method for efficient power system topological analysis

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  • Li, Yiyan
  • Zhou, Zhenghao
  • Ping, Jian
  • Xu, Xiaoyuan
  • Yan, Zheng
  • Wu, Jianzhong

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.

Suggested Citation

  • Li, Yiyan & Zhou, Zhenghao & Ping, Jian & Xu, Xiaoyuan & Yan, Zheng & Wu, Jianzhong, 2026. "A two-stage AI-powered motif mining method for efficient power system topological analysis," Applied Energy, Elsevier, vol. 416(C).
    • Handle: RePEc:eee:appene:v:416:y:2026:i:c:s0306261926006446
    DOI: 10.1016/j.apenergy.2026.127992
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