A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis
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| Publicado en: | arXiv.org (Dec 8, 2024), p. n/a |
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| Autor Principal: | |
| Outros autores: | , , , , |
| Publicado: |
Cornell University Library, arXiv.org
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| Acceso en liña: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3142728008 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3142728008 | ||
| 045 | 0 | |b d20241208 | |
| 100 | 1 | |a Li, Yiyan | |
| 245 | 1 | |a A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 8, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Search algorithms | ||
| 653 | |a Systems analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Graph theory | ||
| 653 | |a Reliability | ||
| 653 | |a Embedding | ||
| 653 | |a Greedy algorithms | ||
| 653 | |a Topology | ||
| 700 | 1 | |a Zhou, Zhenghao | |
| 700 | 1 | |a Ping, Jian | |
| 700 | 1 | |a Xu, Xiaoyuan | |
| 700 | 1 | |a Zheng, Yan | |
| 700 | 1 | |a Wu, Jianzhong | |
| 773 | 0 | |t arXiv.org |g (Dec 8, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3142728008/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.05957 |