A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916513324204032 |
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| author | Li, Yiyan Zhou, Zhenghao Ping, Jian Xu, Xiaoyuan Yan, Zheng Wu, Jianzhong |
| author_facet | Li, Yiyan Zhou, Zhenghao Ping, Jian Xu, Xiaoyuan Yan, Zheng Wu, Jianzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis Li, Yiyan Zhou, Zhenghao Ping, Jian Xu, Xiaoyuan Yan, Zheng Wu, Jianzhong Systems and Control 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. |
| title | A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.05957 |