An Assessment Method for Power Grid Structural Resilience Based on Topological Feature Extraction

doi:10.11930/j.issn.1004-9649.202506001

Abstract

Abstract:

With the frequent occurrence of extreme weather events and the large-scale integration of distributed energy resources, the operating environment of power grids has become increasingly complex, making enhancing power grid resilience a key to ensure the normal operation of society. To address the issues of traditional resilience assessment methods, such as strong dependence on source-load parameters and difficulty in coping with data uncertainty in practical engineering, this paper proposes a power grid resilience evaluation method integrating topological structure parameters and an adaptive analytic hierarchy process (A-AHP). The indicator of "maximum root node coverage radius" is introduced, combined with structural characteristics like betweenness centrality and closeness centrality, to comprehensively reflect the vulnerable links and overall collaborative capacity of the power grid under multi-unit black start recovery conditions. Through data-driven weight optimization and joint adjustment of classification thresholds, the transparency, interpretability, and classification accuracy of the model are improved. Simulation results show that the proposed method can effectively evaluate the impact of different black start resource allocation schemes on power grid resilience in the absence of detailed source-load parameters.

Key words: power grid resilience, topological structure, black start, maximum root node coverage radius, adaptive analytic hierarchy process

LIU Qixing, LEI Aoyu, ZHAI Zhe, LI Jialu, CHEN Yiping, WU Weimin, LEI Shunbo. An Assessment Method for Power Grid Structural Resilience Based on Topological Feature Extraction[J]. Electric Power, 2025, 58(12): 27-36, 49.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202506001

https://www.electricpower.com.cn/EN/Y2025/V58/I12/27

Figures/Tables 9

Fig.1 Schematic of the maximum root node coverage radius

Fig.2 Flowchart of the structural resilience analysis method

Fig.3 Schematic of the IEEE RTS 24 system

Fig.4 Statistical analysis of system restoration characteristics under different black start unit configurations

Table 1 Statistical analysis of structural parameters and black start success rate

结构参数	Pearson相关系数	Pearson p值	Spearman相关系数	Spearman p值	Logistic系数	Logisticp值	t检验t值	t检验p值
$ {R_{\max }}\left( v \right) $	–0.997	6.61×10^–7	–1.000	0.000	–0.370	0.053	–0.989	0.325
$ {C_{\text{B}}}(v) $	–0.305	0.506	–0.384	0.390	–0.067	NaN	–9.101	4.06×10^–15
$ {C_{\text{C}}}(v) $	–0.305	0.506	–0.380	0.393	–0.110	NaN	–9.101	4.06×10^–15

Fig.5 Comparison of accuracy between A-AHP and other data-driven methods

Fig.6 Comparison of generalization ability of different methods under different training set ratios

Table 2 Universality analysis of the A-AHP method

系统	准确率/%	迁移率/%
RTS-24	78.70	71.30
IEEE-14	75.10	70.10
IEEE-30	79.50	72.90
IEEE-57	82.30	72.40
IEEE-118	85.10	76.80

Fig.7 Comparison of prediction accuracy after removing the maximum root node coverage radius

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