Critical link identification of power system vulnerability based on modified graph attention network
<|reference_start|>Critical link identification of power system vulnerability based on modified graph attention network: With the expansion of the power grid and the increase of the proportion of new energy sources, the uncertainty and random factors of the power grid increase, endangering the safe operation of the system. It is particularly important to find out the critical links of vulnerability in the power grid to ensure the reliability of the power grid operation. Aiming at the problem that the identification speed of the traditional critical link of vulnerability identification methods is slow and difficult to meet the actual operation requirements of the power grid, the improved graph attention network (IGAT) based identification method of the critical link is proposed. First, the evaluation index set is established by combining the complex network theory and the actual operation data of power grid. Secondly, IGAT is used to dig out the mapping relationship between various indicators and critical links of vulnerability during the operation of the power grid, establish the identification model of critical links of vulnerability, and optimize the original graph attention network considering the training accuracy and efficiency. Thirdly, the original data set is obtained through simulation, and the identification model is trained, verified and tested. Finally, the model is applied to the improved IEEE 30-node system and the actual power grid, and the results show that the proposed method is feasible, and the accuracy and speed are better than that of traditional methods. It has certain engineering utilization value.<|reference_end|>
@article{wang2024critical,
    title={Critical link identification of power system vulnerability based on
  modified graph attention network},
    author={Changgang Wang, Xianwei Wang, Yu Cao, Yang Li, Qi Lv, Yaoxin Zhang},
    journal={Power System Protection and Control 52 (2024) 36-45},
    year={2024},
    doi={10.19783/j.cnki.pspc.231482},
    archivePrefix={arXiv},
    eprint={2409.07785},
    primaryClass={eess.SY cs.SY}
}