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IEEE/CAA Journal of Automatica Sinica

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L. Xu, B. Zhang, C. Dou, and D. Yue, “Distribution network partitioning for voltage regulation using heterogeneous graph neural networks considering cyber-attacks risk,” IEEE/CAA J. Autom. Sinica, 2026. doi: 10.1109/JAS.2026.125795
Citation: L. Xu, B. Zhang, C. Dou, and D. Yue, “Distribution network partitioning for voltage regulation using heterogeneous graph neural networks considering cyber-attacks risk,” IEEE/CAA J. Autom. Sinica, 2026. doi: 10.1109/JAS.2026.125795

Distribution Network Partitioning for Voltage Regulation Using Heterogeneous Graph Neural Networks Considering Cyber-Attacks Risk

doi: 10.1109/JAS.2026.125795
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  • [1]
    Y. Zheng, Y. Wang, X. Meng, S. Li, and H. Chen, “Distributed economic mpc for synergetic regulation of the voltage of an island dc micro-grid,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 734–745, 2024. doi: 10.1109/JAS.2023.123750
    [2]
    M. Zhai, Q. Sun, R. Wang, and H. Zhang, “Containment-based multiple pcc voltage regulation strategy for communication link and sensor faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2045–2055, 2023. doi: 10.1109/JAS.2023.123747
    [3]
    Y. Long, R. T. Elliott, and D. S. Kirschen, “Adaptive coalition formation-based coordinated voltage regulation in distribution networks,” IEEE Trans. Power Systems, vol. 37, no. 3, pp. 2005–2018, 2022. doi: 10.1109/TPWRS.2021.3120195
    [4]
    C. Zhao, G. Li, L. Zhang, B. Zhang, and W. Tang, “A regulation method considering coupled voltage violation and unbalance in three-phase four-wire hybrid ac/dc lvdns with high penetration pvs,” IEEE Trans. Sustainable Energy, vol. 15, no. 2, pp. 1001–1012, 2024. doi: 10.1109/TSTE.2023.3322317
    [5]
    H. Mehrjerdi, S. Lefebvre, M. Saad, and D. Asber, “A decentralized control of partitioned power networks for voltage regulation and prevention against disturbance propagation,” IEEE Trans. Power Systems, vol. 28, no. 2, pp. 1461–1469, 2013. doi: 10.1109/TPWRS.2012.2225154
    [6]
    L. Ding, Q.-L. Han, B. Ning, and D. Yue, “Distributed resiient finite-time secondary control for heterogeneous battery energy storage systems under denial-of-service attacks,” IEEE Trans. Industrial Informatics, vol. 16, no. 7, pp. 4909–4919, 2020. doi: 10.1109/TII.2019.2955739
    [7]
    Z. Li, J. Shi, and M. Ye, “Distributed resilient nash equilibrium seeking for heterogeneous linear systems under false data injection attacks,” in IECON 2023-49th Annual Conf. of the IEEE Industrial Electronics Society, 2023, pp. 1–4.
    [8]
    J. Yang, C. Zhou, S. Yang, H. Xu, and B. Hu, “Anomaly detection based on zone partition for security protection of industrial cyber-physical systems,” IEEE Trans. Industrial Electronics, vol. 65, no. 5, pp. 4257–4267, 2018. doi: 10.1109/TIE.2017.2772190
    [9]
    Q. Liu, X. Cui, Z. Liu, and H. Wang, “Cognitive navigation for intelligent mobile robots: A learning-based approach with topological memory configuration,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1933–1943, 2024. doi: 10.1109/JAS.2024.124332
    [10]
    Z. Wei, H. Zhao, Z. Li, X. Bu, Y. Chen, X. Zhang, Y. Lv, and F.-Y. Wang, “Stgsa: A novel spatial-temporal graph synchronous aggregation model for traffic prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 226–238, 2023. doi: 10.1109/JAS.2023.123033
    [11]
    X. Li, Y. Xu, N. Li, B. Yang, and Y. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 121–134, 2023. doi: 10.1109/JAS.2022.105935

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