A topology adaptive high-speed transient stability assessment scheme based on multi-graph attention network with residual structure

被引:22
|
作者
Huang, Jiyu [1 ,2 ]
Guan, Lin [1 ,2 ]
Su, Yinsheng [3 ]
Yao, Haicheng [3 ]
Guo, Mengxuan [1 ,2 ]
Zhong, Zhi [1 ,2 ]
机构
[1] South China Univ Technol, Sch Elect Power, Guangzhou 510641, Guangdong, Peoples R China
[2] Guangdong Prov Key Lab Intelligent Operat & Contr, Guangzhou 510663, Peoples R China
[3] CSG Power Dispatching & Control Ctr, Guangzhou 510663, Peoples R China
基金
中国国家自然科学基金;
关键词
Fault occurrence; Improved graph attention layer; Piece-wise transient stability index (PSI); Multi-graph attention network with residual structure (ResGAT); High-speed transient stability assessment (HSTSA); PREDICTION;
D O I
10.1016/j.ijepes.2021.106948
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Reliable and fast transient stability assessment (TSA) is significantly required for the power system emergency control. We propose a topology adaptive high-speed transient stability assessment (HSTSA) scheme, where the inputs of the model adopt only the pre-fault state and the dynamics at the fault occurrence snapshot. A novel multi-graph attention network with residual structure (ResGAT) is designed to capture the stability characteristics. ResGAT applies improved graph attention mechanism to enhance its adaptability to the power system topology changes and the residual structure helps to avoid network degeneration. Meanwhile, a new piece-wise transient stability index (PSI) is proposed for the stability level prediction. Integration of both the stability category and the stability level results increase the precision of the HSTSA scheme. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its robustness under various scenarios. .
引用
收藏
页数:9
相关论文
共 50 条
  • [1] A multi-task transient stability assessment method adapted to topology changes using multi-graph sample and aggregate-attention network
    Huang, Lingxiang
    Dong, Kun
    Zhao, Jianfeng
    Liu, Kangli
    Jin, Cheng
    Guo, Xirui
    [J]. FRONTIERS IN ENERGY RESEARCH, 2024, 11
  • [2] A recursive multi-head graph attention residual network for high-speed train wheelset bearing fault diagnosis
    Yuan, Zonghao
    Li, Xin
    Liu, Suyan
    Ma, Zengqiang
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (06)
  • [3] Channel Estimation of OFDM in High-Speed Railway Based on Multi-Scale Residual Attention Network
    Chen, Yong
    Jiang, Fengyuan
    Zhan, Zhixian
    [J]. Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2023, 52 (04): : 512 - 522
  • [4] A wind speed forecasting framework for multiple turbines based on adaptive gate mechanism enhanced multi-graph attention networks
    Wang, Yufeng
    Yang, Zihan
    Ma, Jianhua
    Jin, Qun
    [J]. APPLIED ENERGY, 2024, 372
  • [5] SMRGAT: A traditional Chinese herb recommendation model based on a multi-graph residual attention network and semantic knowledge fusion
    Yang, Xiaoyan
    Ding, Changsong
    [J]. JOURNAL OF ETHNOPHARMACOLOGY, 2023, 315
  • [6] A Global Attention Pooling -Based Graph Learning Scheme for Generator -Level Transient Stability Assessment
    Huang Jiyu
    Guan Lin
    Cai Zihan
    Chen Liukai
    Chen Haoying
    Chen Zhiying
    [J]. 2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM, 2023,
  • [7] Parallel Graph Attention Network Based Transient Stability Assessment Model for Stability Control Strategy Checking
    Zhu, Siting
    Guan, Lin
    Huang, Jiyu
    Chen, Liukai
    [J]. Dianwang Jishu/Power System Technology, 2023, 47 (09): : 3836 - 3846
  • [8] Multi-Graph based Spectrum Sharing Scheme in Vehicular Network with Integration of Heterogenous Spectrum
    Xuan, Yidi
    Guo, Caili
    Feng, Chunyan
    Li, Zheng
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), 2019,
  • [9] Multi-Sensor Graph Transfer Network for Health Assessment of High-Speed Rail Suspension Systems
    Zhang, Dingcheng
    Xie, Min
    Yang, Jingyuan
    Wen, Tao
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (09) : 9425 - 9434
  • [10] Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction
    Ma, Changxi
    Zhao, Mingxi
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2023, 630