A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis

TL;DR

This paper presents a novel two-stage AI-driven approach for efficient motif mining in power system topologies, significantly reducing computational complexity and enabling large-scale analysis.

Contribution

It introduces a representation learning technique for ordered embedding of power system graphs and a greedy algorithm for rapid motif extraction, overcoming computational challenges.

Findings

Effective motif mining demonstrated on 61 circuit models

Reduces computational complexity of motif search

Enables large-scale power system topological analysis

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.