Transient Stability Assessment of Power Systems Based on Multi-scale Graph Attention Network

被引：0

作者：

Fu, Taiguoyi ^{[1
]}

Du, Youtian ^{[1
]}

Lyu, Hao ^{[1
]}

Li, Zonghan ^{[2
]}

Liu, Jun ^{[3
]}

机构：

[1] School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an

[2] National Key Laboratory of Grid Security, China Electric Power Research Institute, Beijing

[3] School of Electrical Engineering, Xi’an Jiaotong University, Xi’an

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2025年 / 49卷 / 03期

关键词：

deep learning; feature extraction; graph attention network; multi-scale subgraph; transient stability assessment;

D O I：

10.7500/AEPS20240318003

中图分类号：

学科分类号：

摘要：

Existing transient stability assessment methods based on graph deep learning consider the topological structure characteristics of power grids. However, the information transmission characteristics among multi-scale subgraphs in the topological structure of power grids are not effectively modeled, resulting in the insufficient capturing of the local and global dynamic coupling relationship of power grids by the stability judgment model, which reduces the stability judgment accuracy of the model under complex perturbations. Therefore, an assessment method for power angle transient stability integrating the information transmission process of multi-scale subgraphs is proposed. Firstly, a k-dimensional graph attention network is proposed and constructed, which regards the different-scale power grid topology subgraphs as the basic unit for feature extraction in graph deep learning. Then, adaptive weights are assigned to the feature aggregation through the attention mechanism to mine the characteristics between different fine-grained regions in the actual power grid. Finally, the feasibility and effectiveness of the proposed method are verified through the CEPRI-TAS-173 system. © 2025 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：60 / 70

页数：10

共 29 条

[1]

Jun LIU, SUN Huiwen, WU Liu, Et al., Overview of transient stability assessment of power systems[J], Smart Power, 47, 12, pp. 44-53, (2019)

[2]

YANG Jun, HOU Junhao, LIU Yawei, Et al., Distributed cooperative control method and application in power system[J], Transactions of China Electrotechnical Society, 36, 19, pp. 4035-4049, (2021)

[3]

WANG Weisheng, LIN Weifang, HE Guoqing, Et al., Enlightenment of 2021 Texas blackout to the renewable energy development in China[J], Proceedings of the CSEE, 41, 12, pp. 4033-4043

[4]

LUO Shenzeng, LI Yinhong, SHI Dongyuan, Wide area monitoring system observability probabilistic evaluation and it’s application in optimal PMU placement[J], Transactions of China Electrotechnical Society, 33, 8, pp. 1844-1853, (2018)

[5]

XIE Yanxiang, XIAO Han, LI Jiayi, Et al., Research status and prospect of transient stability assessment methods of power system[J], Electrotechnics Electric, 11, pp. 1-9, (2020)

[6]

LI Yuting, LIU Jun, LIU Jiacheng, Et al., Transient stability assessment based on imbalanced sample enhancement of denoising diffusion probabilistic model [J], Automation of Electric Power Systems, 48, 21, pp. 148-157, (2024)

[7]

CHEN Houhe, WANG Changjiang, JIANG Tao, Et al., Transient stability assessment in bulk power grid using projection energy function and support vector machine with pinball loss[J], Transactions of China Electrotechnical Society, 32, 11, pp. 67-76, (2017)

[8]

GOMEZ F R, RAJAPAKSE A D，, ANNAKKAGE U D, Et al., Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements[J], IEEE Transactions on Power Systems, 26, 3, pp. 1474-1483, (2011)

[9]

LI Bingyang, XIAO Jianmei, WANG Xihuai, Power system transient stability assessment based on hybrid neighborhood rough reduction and deep forest [J], Transactions of China Electrotechnical Society, 35, 15, pp. 3245-3257, (2020)

[10]

ZHEN Yongzan, RUAN Cheng, Hierarchical self-adaptation transient voltage stability assessment based on cost-sensitive SVM and multivariate decision tree [J], Power System Technology, 48, 2, pp. 778-791, (2024)

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