TY - JOUR
T1 - A data-driven distributed and easy-to-transfer method for short-term voltage stability assessment
AU - Cai, Huaxiang
AU - Hill, David J.
N1 - Funding Information:
The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region under General Research Fund through Project No. 17208817 .
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/7
Y1 - 2022/7
N2 - In this paper, a novel data-driven method named Gated Recurrent Graph Attention Network (GRGAT) for STVS assessment is developed by learning the relationship between system dynamics during faults and the corresponding transient voltage security index (TVSI). GRGAT can capture the spatial–temporal correlation in the power system, because the attention operations of bus information are performed directly on the system topology and the system dynamics are captured with gated recurrent units. Particularly, all operations are independent between buses. Therefore, GRGAT is not only distributed during online application, but also easy-to-transfer, which can adapt to the change of topological structures. To show the feasibility, adversarial adaptation is adopted to transfer learned knowledge for another modified network. The effectiveness and efficiency of GRGAT are demonstrated on the New England 10-Generator-39-Bus system and its modified systems. Simulation results also show the potential of this learning technique in knowledge transfer.
AB - In this paper, a novel data-driven method named Gated Recurrent Graph Attention Network (GRGAT) for STVS assessment is developed by learning the relationship between system dynamics during faults and the corresponding transient voltage security index (TVSI). GRGAT can capture the spatial–temporal correlation in the power system, because the attention operations of bus information are performed directly on the system topology and the system dynamics are captured with gated recurrent units. Particularly, all operations are independent between buses. Therefore, GRGAT is not only distributed during online application, but also easy-to-transfer, which can adapt to the change of topological structures. To show the feasibility, adversarial adaptation is adopted to transfer learned knowledge for another modified network. The effectiveness and efficiency of GRGAT are demonstrated on the New England 10-Generator-39-Bus system and its modified systems. Simulation results also show the potential of this learning technique in knowledge transfer.
KW - Adversarial adaptation
KW - Distributed structure
KW - Gated recurrent unit
KW - Graph attention
KW - Knowledge transfer
KW - Short-term voltage stability
UR - http://www.scopus.com/inward/record.url?scp=85124649962&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2022.107960
DO - 10.1016/j.ijepes.2022.107960
M3 - Article
AN - SCOPUS:85124649962
SN - 0142-0615
VL - 139
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 107960
ER -