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| Research on Subsynchronous Oscillation Suppression Strategy of PV Grid-Connected Systemvia Koopman-Based Response-Driven Predictive Control |
| Wang Zihan1, Zheng Le1, Cao Junqi1, Wu Powei2, Li Jiayan1, Li Gengyin1 |
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China;
2. State Grid Beijing Electric Power Company Beijing 100124 China |
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Abstract Under the background of the development of the power system driven by the goals of “carbon peaking” and “carbon neutrality”, renewable energy sources represented by wind and photovoltaic (PV) power in China have witnessed leapfrog growth, which are gradually becoming the main primary power sources of the power system. Due to the inherent weak anti-interference ability and low damping characteristics of PVs, their adverse dynamic interactions with the power grid have significantly exacerbated the risk of subsynchronous oscillation (SSO) instability in the power system. The PV grid-connected system exhibits dynamic characteristics of high- dimensional complexity, strong nonlinearity, and significant uncertainty, rendering traditional model-based SSO suppression strategies more prone to failure. Therefore, to address this issue, this paper builds a bridge between response information and system behavior, and proposes a SSO robust suppression strategy for PV grid- connected systems via Koopman-based response-driven predictive control.
Firstly, the system is reconstructed using finite time-domain data based on behavioral system theory, and then the dynamic behavior of nonlinear systems is accurately identified in the Koopman linearized observable space. Secondly, the identification, prediction, and control of system behavior are integrated into an optimal control strategy design, and regularization and relaxation techniques are introduced to enhance control robustness. Subsequently, online SSO suppression is accomplished through rolling optimization. Thirdly, theoretical analysis reveals the robustness enhancement mechanism of regularization and relaxation techniques in the suppression strategy, and a fast solution method for this strategy is developed based on the analytical expression of the optimal control law. Finally, through time-domain simulations and hardware-in-the-loop (HIL) experiments, the effectiveness, robustness, and practicability of the SSO suppression strategy are verified in uncertain conditions such as noise interference, parameter variations, source/load fluctuations, and power grid topology changes.
The main conclusions of this paper are summarized as follows:
(1) A behavioral system theory combined with the Koopman operator is proposed to realize finite-time domain data reconstruction of nonlinear systems, establishing a bridge between response information and the dynamic behavior of complex systems. Simulation results show that the proposed response-driven prediction model accurately identifies the SSO behavior of PV grid-connected systems with significant nonlinear characteristics.
(2) A Koopman response-driven predictive control framework for SSO suppression is constructed, organically integrating system behavior identification, prediction, and control into a linear quadratic optimization problem. This integration enables online multi-step receding horizon optimization, which effectively addresses bounded disturbances and suppresses SSOs.
(3) Based on Matlab time-domain simulations and HIL experiments, the effectiveness, robustness, and practicality of the proposed suppression strategy are verified in different uncertain conditions. Case studies indicate that compared to linear control strategies or those based on deterministic equality constraints, the proposed response-driven suppression strategy yields more accurate predictions, providing technical control support to enhance the dynamic stability of PV grid-connected systems.
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Received: 22 January 2025
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[1] “十四五”可再生能源发展规划[EB/OL]. http://zfxxgk.nea.gov.cn/1310611148_16541341407541n.pdf,2022.6.1.
14th Five-Year Plan for Renewable Energy Development[EB/OL]. http://zfxxgk.nea.gov.cn/1310611148_16541341407541n.pdf,2022.6.1.
[2] 姚玉璧, 郑绍忠, 杨扬, 等. 中国太阳能资源评估及其利用效率研究进展与展望[J]. 太阳能学报, 2022, 43(10): 524-535.
Yao Yubi, Zheng Shaozhong, Yang Yang, et al.Progress and prospects on solar energy resource evaluation and utilization efficiency in China[J]. Acta Energiae Solaris Sinica, 2022, 43(10): 524-535.
[3] Cheng Yunzhi, Fan Lingling, Rose J, et al.Real-world subsynchronous oscillation events in power grids with high penetrations of inverter-based resources[J]. IEEE Transactions on Power Systems, 2023, 38(1): 316-330.
[4] 肖湘宁, 罗超, 廖坤玉. 新能源电力系统次同步振荡问题研究综述[J]. 电工技术学报, 2017, 32(6): 85-97.
Xiao Xiangning, Luo Chao, Liao Kunyu.Review of the research on subsynchronous oscillation issues in electric power system with renewable energy sources[J]. Transactions of China Electrotechnical Society, 2017, 32(6): 85-97.
[5] 谢小荣, 刘华坤, 贺静波, 等. 电力系统新型振荡问题浅析[J]. 中国电机工程学报, 2018, 38(10): 2821-2828, 3133.
Xie Xiaorong, Liu Huakun, He Jingbo, et al.On new oscillation issues of power systems[J]. Proceedings of the CSEE, 2018, 38(10): 2821-2828, 3133.
[6] 陈露洁, 徐式蕴, 孙华东, 等. 高比例电力电子电力系统宽频带振荡研究综述[J]. 中国电机工程学报, 2021, 41(7): 2297-2310.
Chen Lujie, Xu Shiyun, Sun Huadong, et al.A survey on wide-frequency oscillation for power systems with high penetration of power electronics[J]. Proceedings of the CSEE, 2021, 41(7): 2297-2310.
[7] 马文杰, 张波, 丘东元, 等. 跟网型并网变换器的稳定域重塑控制策略研究综述[J]. 电气工程学报, 2023, 18(2): 34-51.
Ma Wenjie, Zhang Bo, Qiu Dongyuan, et al.Control strategy to reshape the stable region for grid- following converter: an overview[J]. Journal of Electrical Engineering, 2023, 18(2): 34-51.
[8] 高本锋, 邓鹏程, 梁纪峰, 等. 光伏电站与弱交流电网间次同步交互作用路径及阻尼特性分析[J]. 电工技术学报, 2023, 38(24): 6679-6694.
Gao Benfeng, Deng Pengcheng, Liang Jifeng, et al.Analysis of path and damping characteristics of subsynchronous interaction between photovoltaic plant and weak AC grid[J]. Transactions of China Electrotechnical Society, 2023, 38(24): 6679-6694.
[9] Yan Gangui, Wang Zhenyang, Zhao Yue, et al.Analysis and suppression of sub-synchronous oscillation of photovoltaic power generation based on damping torque method[J]. IEEE Transactions on Industry Applications, 2024, 60(3): 5074-5083.
[10] 张帆, 高本锋, 李铁成. 基于SVG的光伏并网SSO附加阻尼抑制策略[J]. 中国电力, 2021, 54(12): 11-19, 44.
Zhang Fan, Gao Benfeng, Li Tiecheng.SVG based supplementary damping mitigation strategy for sub- synchronous oscillation in grid-connected photo- voltaic plants[J]. Electric Power, 2021,54(12): 11-19, 44.
[11] 伍珀苇, 王子涵, 李庚银, 等. 基于模型预测控制的光伏并网系统次同步振荡抑制策略研究[J]. 太阳能学报, 2024, 45(8): 349-357.
Wu Powei, Wang Zihan, Li Gengyin, et al.Research on subsynchronous oscillation suppression strategy of photovoltaic grid-connected system based on model predictive control[J]. Acta Energiae Solaris Sinica, 2024, 45(8): 349-357.
[12] Geng Qi, Sun Huadong, Zhang Xing, et al.Mitigation of oscillations in three phase LCL-filtered grid converters based on proportional resonance and improved model predictive control[J]. IEEE Transa- ctions on Industry Applications, 2023, 59(2): 2590-2602.
[13] Richalet J, Rault A, Testud J L, et al.Model predi- ctive heuristic control[J]. Automatica, 1978, 14(5): 413-428.
[14] 青辰, 魏震波, 刘洋, 等. 基于双时间尺度模型预测控制的灵活性资源动态调度[J]. 高压电器, 2025, 61(5): 31-40, 52.
Qing Chen, Wei Zhenbo, Liu Yang, et al.Dynamic scheduling of flexible resources based on dual-time scale model predictive control[J]. High Voltage Apparatus, 2025, 61(5): 31-40, 52.
[15] Hu Jiefeng, Shan Yinghao, Yang Yong, et al.Economic model predictive control for microgrid optimization: a review[J]. IEEE Transactions on Smart Grid, 2024, 15(1): 472-484.
[16] 陈剑, 杜文娟, 王海风. 基于对抗式迁移学习的含柔性高压直流输电的风电系统次同步振荡源定位[J]. 电工技术学报, 2021, 36(22): 4703-4715.
Chen Jian, Du Wenjuan, Wang Haifeng.Location method of subsynchronous oscillation source in wind power system with VSC-HVDC based on adversarial transfer learning[J]. Transactions of China Electro- technical Society, 2021, 36(22): 4703-4715.
[17] 甄永赞, 狄依容, 胡永强, 等. 数据驱动的风光场站次同步振荡多机协同阻尼控制方法[J]. 电工技术学报, 2024, 39(18): 5855-5867, 5898.
Zhen Yongzan, Di Yirong, Hu Yongqiang,et al.Data-driven multi-machine cooperative damping control for wind and photovoltaic plants restraining sub-synchronous oscillation[J]. Transactions of China Electrotechnical Society, 2024, 39(18): 5855-5867, 5898.
[18] Yousefian R, Kamalasadan S.Energy function inspired value priority based global wide-area control of power grid[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 552-563.
[19] Surinkaew T, Emami K, Shah R, et al.Forced oscillation management in a microgrid with distri- buted converter-based resources using hierarchical deep-learning neural network[J]. Electric Power Systems Research, 2023, 222: 109479.
[20] Willems J C.From time series to linear system: Part III Approximate modelling[J]. Automatica, 1987, 23(1): 87-115.
[21] Willems J C, Rapisarda P, Markovsky I, et al.A note on persistency of excitation[J]. Systems & Control Letters, 2005, 54(4): 325-329.
[22] Koopman B O.Hamiltonian systems and transfor- mation in Hilbert space[J]. Proceedings of the National Academy of Sciences of the United States of America, 1931, 17(5): 315-318.
[23] 郑乐, 王子涵, 沈沉, 等. Koopman算符理论在新型电力系统分析与控制中的应用与挑战[J/OL]. 中国电机工程学报, 2025. http://kns.cnki.net/kcms/detail/11.2107.tm.20250220.1347.008.html.
Zheng L, Wang Z H, Shen C, et al. The application and challenge of Koopman operator in new-type power system[J/OL]. Proceedings of the CSEE, 2025. http://kns.cnki.net/kcms/detail/11.2107.tm.20250220.1347.008.html.
[24] 张子傲, 李岩松, 任必兴, 等. 电力系统Koopman动态等值方法[J]. 电工技术学报, 2026, 41(1): 111-126.
Zhang Ziao, Li Yansong, Ren Bixing, et al.Dynamic equivalencing method for power systems based on Koopman theory[J]. Transactions of China Electro- technical Society, 2026, 41(1): 111-126.
[25] 刘豹, 唐万生. 现代控制理论[M]. 3版. 北京: 机械工业出版社, 2006.
[26] Rowley C W, Mezić I, Bagheri S, et al.Spectral analysis of nonlinear flows[J]. Journal of Fluid Mechanics, 2009, 641: 115-127.
[27] Tu J H, Rowley C W, Luchtenburg D M, et al.On dynamic mode decomposition: Theory and appli- cations[J]. Journal of Computational Dynamics, 2014, 1(2): 391-421.
[28] Zheng Le, Liu Xin, Xu Yanhui, et al.Data-driven estimation for a region of attraction for transient stability using the Koopman operator[J]. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1405-1413.
[29] Wang Z, Huang Z, Zhang X, et al. Data-driven subsynchronous oscillation suppression for renewable energy integrated power systems based on Koopman operator[J/OL]. CSEE Journal of Power and Energy Systems, 2025. https://ieeexplore.ieee.org/abstract/document/10838276.
[30] Lappalainen K, Valkealahti S.Output power variation of different PV array configurations during irradiance transitions caused by moving clouds[J]. Applied Energy, 2017, 190: 902-910.
[31] Coulson J, Lygeros J, Dörfler F.Data-enabled predictive control: in the shallows of the DeePC[C]// 2019 18th European Control Conference (ECC), Naples, Italy, 2019: 307-312.
[32] Sedghizadeh S, Beheshti S.Data-driven subspace predictive control: Stability and horizon tuning[J]. Journal of the Franklin Institute, 2018, 355(15): 7509-7547.
[33] 叶华, 刘玉田. 基于在线递推闭环子空间辨识的模型预测阻尼控制[J]. 中国电机工程学报, 2009, 29(28): 55-61.
Ye Hua, Liu Yutian.Model predictive damping control based on on-line recursive closed-loop subspace identification[J]. Proceedings of the CSEE, 2009, 29(28): 55-61.
[34] Al Hasnain F, Hossain S J, Kamalasadan S.A novel hybrid deterministic-stochastic recursive subspace identification for electromechanical mode estimation, classification, and control[J]. IEEE Transactions on Industry Applications, 2021, 57(5): 5476-5487.
[35] Chen Runze, Sun Hongbin, Guo Qinglai, et al.Reducing generation uncertainty by integrating CSP with wind power: an adaptive robust optimization- based analysis[J]. IEEE Transactions on Sustainable Energy, 2015, 6(2): 583-594.
[36] Tian Yingjie, Zhang Yuqi.A comprehensive survey on regularization strategies in machine learning[J]. Information Fusion, 2022, 80: 146-166. |
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2026, Vol. 41 
