A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
IEEE/CAA Journal of Automatica Sinica
| Citation: | Z. Wu, M. Zhang, B. Fan, Y. Shi, and X. Guan, “Deep synchronization control for grid-forming converters: A reinforcement learning approach,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–3, Jan. 2025.
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N. Malla, U. Tamrakar, D. Shrestha, Z. Ni, and R. Tonkoski, “Online learning control for harmonics reduction based on current controlled voltage source power inverters,” IEEE/CAA J. Automatica Sinica, vol. 4, no. 3, pp. 447–457, 2017. doi: 10.1109/JAS.2017.7510541
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X. Fu, J. Sun, M. Huang, Z. Tian, H. Yan, H. H.-C. Iu, P. Hu, and X. Zha, “Large-signal stability of grid-forming and grid-following controls in voltage source converter: A comparative study,” IEEE Trans. Power Electronics, vol. 36, no. 7, pp. 7832–7840, 2020.
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Z. Shen, J. Zhu, L. Ge, S. Bu, J. Zhao, C. Y. Chung, X. Li, and C. Wang, “Variable-inertia emulation control scheme for VSC-hvdc transmission systems,” IEEE Trans. Power Syst., vol. 37, no. 1, pp. 629–639, 2021.
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S. M. Azizi, “Robust controller synthesis and analysis in inverter-dominant droop-controlled islanded microgrids,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1401–1415, 2021. doi: 10.1109/JAS.2021.1004006
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Y. Jiang, R. Pates, and E. Mallada, “Dynamic droop control in low-inertia power systems,” IEEE Trans. Autom. Control, vol. 66, no. 8, pp. 3518–3533, 2020.
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Q.-C. Zhong and G. Weiss, “Synchronverters: Inverters that mimic synchronous generators,” IEEE Trans. Industrial Electronics, vol. 58, no. 4, pp. 1259–1267, 2010.
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Q.-C. Zhong, P.-L. Nguyen, Z. Ma, and W. Sheng, “Self-synchronized synchronverters: Inverters without a dedicated synchronization unit,” IEEE Trans. Power Electronics, vol. 29, no. 2, pp. 617–630, 2013.
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D. Raisz, T. T. Thai, and A. Monti, “Power control of virtual oscillator controlled inverters in grid-connected mode,” IEEE Trans. Power Electronics, vol. 34, no. 6, pp. 5916–5926, 2018.
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A. Ademola-Idowu and B. Zhang, “Frequency stability using mpc-based inverter power control in low-inertia power systems,” IEEE Trans. on Power Systems, vol. 36, no. 2, pp. 1628–1637, 2020.
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O. Stanojev, U. Markovic, P. Aristidou, G. Hug, D. Callaway, and E. Vrettos, “Mpc-based fast frequency control of voltage source converters in low-inertia power systems,” IEEE Trans. Power Systems, vol. 37, no. 4, pp. 3209–3220, 2020.
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O. Stanojev, O. Kundacina, U. Markovic, E. Vrettos, P. Aristidou, and G. Hug, “A reinforcement learning approach for fast frequency control in low-inertia power systems,” in Proc. 52nd North American Power Symposium, IEEE, pp. 1–6, 2021.
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Y. Li, W. Gao, S. Huang, R. Wang, W. Yan, V. Gevorgian, and D. W. Gao, “Data-driven optimal control strategy for virtual synchronous generator via deep reinforcement learning approach,” J. Modern Power Systems and Clean Energy, vol. 9, no. 4, pp. 919–929, 2021. doi: 10.35833/MPCE.2020.000267
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Q. Yang, L. Yan, X. Chen, Y. Chen, and J. Wen, “A distributed dynamic inertia-droop control strategy based on multi-agent deep reinforcement learning for multiple paralleled vsgs,” IEEE Trans. Power Systems, 2022.
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P. S. Kundur and O. P. Malik, Power System Stability and Control. McGraw-Hill Education, 2022.
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C. De Persis and N. Monshizadeh, “Bregman storage functions for microgrid control,” IEEE Trans. Autom. Control, vol. 63, no. 1, pp. 53–68, 2017.
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J. Z. Kolter and G. Manek, “Learning stable deep dynamics models,” Advances in Neural Information Processing Systems, vol. 32, 2019.
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T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
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