A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis
محفوظ في:
| الحاوية / القاعدة: | arXiv.org (Dec 8, 2024), p. n/a |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , , |
| منشور في: |
Cornell University Library, arXiv.org
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full text outside of ProQuest |
| الوسوم: |
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| مستخلص: | 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. |
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| تدمد: | 2331-8422 |
| المصدر: | Engineering Database |