面向电力系统快速频率响应的数据与模型驱动预测控制

吴卓睿; 张萌; 管晓宏

doi:10.16383/j.aas.c250261

面向电力系统快速频率响应的数据与模型驱动预测控制

doi: 10.16383/j.aas.c250261 cstr: 32138.14.j.aas.c250261

1.
西安交通大学网络空间安全学院西安 710049

基金项目: 国家自然科学基金(62033005, 62273270), 能源陕西实验室项目(ESLB202413)资助

详细信息

作者简介:
吴卓睿：西安交通大学网络空间安全学院博士研究生. 2021年获得西安交通大学学士学位. 主要研究方向为数据驱动电力系统控制. E-mail: wzr445576941@stu.xjtu.edu.cn

张萌：西安交通大学网络空间安全学院教授. 2018年获得浙江大学博士学位. 主要研究方向为电力系统控制、优化与安全. 本文通信作者. E-mail: mengzhang2009@xjtu.edu.cn

管晓宏：中国科学院院士, 西安交通大学电子与信息学部教授. 1993年获得美国康涅狄格大学博士学位. 主要从事能源电力系统优化与安全理论及应用研究. E-mail: xhguan@mail.xjtu.edu.cn

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出版历程
- 收稿日期: 2025-06-16
- 录用日期: 2025-08-19
- 网络出版日期: 2025-09-08

Data and Model-driven Predictive Control for Fast Frequency Response in Power Systems

1.
School of Cyber Sicence and Engineering, Xi＇an Jiaotong University, Xi＇an 710049

Funds: Supported by National Natural Science Foundation of China (62033005, 62273270), and S&T Program of Energy Shaanxi Laboratory (ESLB202413)

More Information

Author Bio:
WU Zhuo-Rui　Ph.D. candidate at the School of Cyber Sicence and Engineering, Xi′an Jiaotong University. He received his bachelor degree from Xi′an Jiaotong University in 2012. His main research interest is data-driven power system control

ZHANG Meng　Professor at the School of Cyber Sicence and Engineering, Xi＇an Jiaotong University. He received his Ph.D. degree from Zhejiang University in 2018. His research interest covers control, optimization and security of power sytems. Corresponding author of this paper

GUAN Xiao-Hong　Member of the Chinese Academy of Sciences, professor at the Faculty of Electronic and Information Engineering, Xi′an Jiaotong University. He received his Ph.D. degree from the University of Connecticut in 1993. His research interest covers energy and power system optimization and security theory and application research

摘要

摘要: 维持频率稳定是电力系统控制的一个重要目标. 然而, 高渗透率新能源可能导致频繁的功率波动, 对系统频率调节造成不利影响. 为解决这一问题, 通常需要快速调节变流器资源的功率输出, 响应系统频率波动以实现快速频率控制. 针对电力系统快速频率控制, 提出一种数据与模型驱动的预测控制方法. 首先, 设计数据驱动的扰动观测器以估计负荷变化与新能源波动等系统扰动. 为优化控制性能, 利用基于神经网络设计的参考调节器为模型预测控制器提供虚拟参考. 通过学习长预测时域模型预测控制器, 参考调节器能够提升短预测时域控制器性能, 因而降低了所需的计算时间. 最终, 仿真对比结果表明所提方法能够有效提高频率控制性能.
- 快速频率控制 /
- 变流器资源 /
- 模型预测控制 /
- 参考调节器 /
- 深度学习
Abstract: A key objective of power system control is to maintain frequency stability. However, high penetration of renewable energy resources may cause frequent power fluctuations, resulting in negative impact on system frequency regulation. To deal with this issue, it commonly requires to rapidly adjust the power output of inverter-based resources in response to system frequency fluctations, achieving fast frequency control. This paper proposes a data and model-driven predictive control method for fast frequency control in power systems. First, a data-driven disturbance observer is designed to estimate system disturbance such as load changes, renewable energy fluctuations and so on. To optimize the control performance, the reference governor designed by a neural network provides virtual reference for the model predictive controller. The reference governor enhances the performance of the controller with a short prediction horizon through learning a long prediction horizon controller, thus reducing the required computation time. Finally, simulation comparison results demonstrate that the proposed method can effectively improve the frequency control performance.
- Fast frequency control /
- inverter-based resource /
- model predictive control /
- reference governor /
- deep learning

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图 1 系统频率响应模型

Fig. 1 System frequency response model

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图 2 快速频率控制框图

Fig. 2 Block diagram of fast frequency control

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图 3 不同预测时域下系统频率稳态误差

Fig. 3 Steady-state error of the system frequency under different prediction horizons

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图 4 基于深度学习的参考调节器方法框架

Fig. 4 The framwork of the deep learning-based reference governor

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图 5 IEEE 39母线测试系统

Fig. 5 IEEE 39 bus test system

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图 6 参考调节器训练结果

Fig. 6 Training results of the reference governors

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图 7 负荷阶跃扰动下频率动态

Fig. 7 Frequency dynamics under the step load disturbance

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图 8 负荷阶跃扰动下IBR有功输出

Fig. 8 Active output of IBR under step load disturbance

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图 9 负荷阶跃扰动下参考调节器输出的虚拟参考

Fig. 9 Virtual references obtained from reference governors under step load disturbance

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图 10 输出功率限制下的频率动态与有功输出

Fig. 10 Frequency dynamics and active power output under limited power output

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图 11 风力发电功率变化

Fig. 11 Change of wind power

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图 12 风力发电波动下的频率动态

Fig. 12 Frequency dynamics under wind power fluctations

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图 13 风力发电波动下的IBR有功输出

Fig. 13 Active output of IBR under wind power fluctations

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图 14 线路跳闸下的频率动态

Fig. 14 Frequency dynamics under line tripping

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图 15 线路跳闸下的IBR有功输出

Fig. 15 Active output of IBR under line tripping

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表 1 不同预测时域下参考调节器训练误差

Table 1 Training error of the reference governor with different prediction horizons

预测时域	$ T = 2 $	$ T = 3 $	$ T = 5 $	$ T = 10 $
训练误差	447	4.14	0.686	0.130

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表 2 负荷阶跃扰动下四种控制器性能指标对比

Table 2 Comparison of performance metrics of the four controllers under step load disturbance

控制器	最大频率偏差/Hz	稳态频率偏差/Hz	计算时间/ms
w/o DRG ($ T = 5 $)	0.0322	0.0232	0.91
w/o DRG ($ T = 30 $)	0.0241	0.0017	5.26
w/ DRG1 ($ T = 5 $)	0.0218	0.0016	1.12
w/ DRG ($ T = 5 $)	0.0168	0.0002	1.05

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表 3 风力发电波动下四种控制器性能指标对比

Table 3 Comparison of performance metrics of the four controllers under wind power fluctations

控制器	最大频率偏差/Hz	稳态频率偏差/Hz	计算时间/ms
w/o DRG ($ T=5 $)	0.0442	0.0372	0.91
w/o DRG ($ T=30 $)	0.0419	0.0026	5.17
w/ DRG1 ($ T=5 $)	0.0392	0.0025	1.00
w/ DRG2 ($ T=5 $)	0.0320	0.0001	1.08

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