[1]王明东,杨岙迪,李龙好,等.基于VSG 下垂优化控制的新能源电力系统惯性提升[J].郑州大学学报(工学版),2024,45(03):127-133.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 005]
　WANG Mingdong,YANG Aodi,LI Longhao,et al.Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):127-133.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 005]
点击复制

基于VSG 下垂优化控制的新能源电力系统惯性提升()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年03期
页码:
127-133
栏目:
出版日期:
2024-04-20

文章信息/Info

Title:
Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control
文章编号:
1671-6833( 2024) 03-0127-07
作者:
王明东1;  杨岙迪1;  李龙好2;  李忠文1
1. 郑州大学 电气与信息工程学院,河南 郑州 450001;2. 许继电气股份有限公司,河南 许昌 461000
Author(s):
WANG Mingdong1; YANG Aodi1; LI Longhao2; LI Zhongwen1
1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. XJ Electric Co. ,Ltd. , Xuchang 461000,China
关键词:
新能源电力系统;  VSG;  下垂控制;  神经网络;  小信号分析模型
Keywords:
new energy power system;  VSG;  droop control;  neural network;  small signal analysis model
分类号:
TM712TP13
DOI:
10. 13705/ j. issn. 1671-6833. 2024. 03. 005
文献标志码:
A
摘要:
针对传统VSG 技术动态性能较差且重要参数J 和D 最优值较难确定的问题,提出了一种基于下垂控制与神经网络预测的VSG 控制与参数优化策略,实现了VSG 技术中关键参数J 和D 的动态调节。首先,所提策略将有功功率-频率下垂控制应用于VSG 的控制算法中;其次,通过模拟同步发电机转子运动方程和电压与无功控制特性,建立VSG 的小信号分析模型,完成了关键参数转动惯量与阻尼系数的初值整定;最后,建立了人工神经网络进行分析学习和网络训练,调整权值以改变VSG 转动惯量与阻尼系数,通过误差函数比较输出量与输入量之间的误差,多次学习训练后参数达到期望值。将神经网络优化算法与下垂控制策略结合,对VSG 控制策略进行优化。分别采用传统VSG 控制、恒定参数下垂控制和基于神经网络优化的自适应参数下垂控制对算例进行仿真,结果表明:所提基于神经网络优化的自适应参数下垂控制比传统VSG 控制的频率最大变化量降低了26. 7%,频率稳定时间降低了0. 25 s,表明了所提策略的有效性。
Abstract:
Aiming at the problems of poor dynamic performance of traditional VSG technology and difficulty to determine the optimal values of important parameters J and D, a VSG control and parameter optimization strategy based on droop control and neural network prediction was proposed to realize dynamic adjustment of key parameters J and D in VSG technology. The proposed strategy applied the active power-frequency droop control to the control algorithm of VSG. Then, simulated the rotor motion equation and the voltage and reactive power control characteristics of synchronous generator, the small signal analysis model of VSG was established, and the initial setting of key parameters rotational inertia and damping coefficient were completed. Finally, an artificial neural network was established for analysis learning and network training, and the weight was adjusted to change the VSG moment of inertia and damping coefficient. The error between the output and the input was compared by the error function, and the parameter reached the expected value after multiple learning and training. The neural network optimization algorithm was combined with the droop control strategy to optimize the VSG control strategy. Traditional VSG control, constant parameter droop control and adaptive parameter droop control based on neural network optimization were used to simulate a numerical example, and the results showed that, compared with traditional VSG control, the proposed adaptive parameter droop control based on neural network optimization reduced the maximum frequency variation by 26.7%, and the frequency stabilization time by 0.25 s. The strategy was effective.

参考文献/References:

[1] 鲁宗相, 汤海雁, 乔颖,等. 电力电子接口对电力系统 频率控制的影响综述[J]. 中国电力, 2018, 51(1): 51-58. LU Z X, TANG H Y, QIAO Y, et al. The impact of power electronics interfaces on power system frequency control: a review[J]. Electric Power, 2018, 51(1): 51 -58.

[2] 李雪, 宋彦龙. 蓄电池储能运行控制对有源配电网影 响研究[J]. 郑州大学学报(工学版), 2019, 40(5): 32-38, 51. LI X, SONG Y L. Study on the influence of battery energy storage operation control on active distribution network [J]. Journal of Zhengzhou University (Engineering Science), 2019, 40(5): 32-38, 51.
[3] SONI N, DOOLLA S, CHANDORKAR M C. Improvement of transient response in microgrids using virtual inertia[ J]. IEEE Transactions on Power Delivery, 2013, 28 (3): 1830-1838.
[4] ATTYAABT, HARTKOPF T. Control and quantification of kinetic energy released by wind farms during power system frequency drops[J]. IET Renewable Power Generation, 2013, 7(3): 210-224.
[5] 李少林, 秦世耀, 王瑞明,等. 大容量双馈风电机组虚 拟惯量调频技术[ J]. 电力自动化设备, 2018, 38 (4): 145-150, 156. LI S L, QIN S Y, WANG R M, et al. Control strategy of virtual inertia frequency regulation for large capacity DFIG-based wind turbine[J]. Electric Power Automation Equipment, 2018, 38(4): 145-150, 156.
[6] 曾正, 赵荣祥, 汤胜清,等. 可再生能源分散接入用先 进并网逆变器研究综述[ J]. 中国电机工程学报, 2013, 33(24): 1-12, 21. ZENG Z, ZHAO R X, TANG S Q, et al. An overview on advanced grid-connected inverters used for decentralized renewable energy resources [ J ]. Proceedings of the CSEE, 2013, 33(24): 1-12, 21.
[7] KE Z P, DAI Y X, PENG Z S, et al. VSG control strategy incorporating voltage inertia and virtual impedance for microgrids[J]. Energies, 2020, 13(16): 4263.
[8] RASOOL A, YAN X W, RASOOL H, et al. VSG stability and coordination enhancement under emergency condition[ J]. Electronics, 2018, 7(9): 202.
[9] HUL L, FU L. Primary frequency modulation of microgrid based on consistent droop control method[J]. Journal of Physics: Conference Series, 2022, 2387(1): 012017.
[10] 罗兰, 王渝红, 陈诗昱,等. 基于虚拟同步发电机控制 策略的多端柔性直流系统自适应下垂控制[J]. 科学 技术与工程, 2021, 21(17): 7116-7121. LUO L, WANG Y H, CHEN S Y, et al. Adaptive droop control of multi-terminal direct current based on virtual synchronous generator control strategy[J]. Science Technology and Engineering, 2021, 21(17): 7116-7121.
[11] 孙孝峰, 王娟, 田艳军,等. 基于自调节下垂系数的 DG 逆变器控制[J]. 中国电机工程学报, 2013, 33 (36): 71-78, 11. SUN X F, WANG J, TIAN Y J, et al. Control of DG connected inverters based on self-adaptable adjustment of droop coefficient[ J]. Proceedings of the CSEE, 2013, 33(36): 71-78, 11.
[12] TORRES M, LOPES L A C. Virtual synchronous generator control in autonomous wind-diesel power systems[C] ∥2009 IEEE Electrical Power & Energy Conference (EPEC). Piscataway:IEEE, 2009: 1-6.
[13] KERDPHOL T, RAHMAN F S, WATANABE M, et al. Small-signal analysis of multiple virtual synchronous machines to enhance frequency stability of grid-connected high renewables [J]. IET Generation, Transmission & Distribution, 2021, 15(8): 1273-1289.
[14] CHENY, HESSE R, TURSCHNER D, et al. Improving the grid power quality using virtual synchronous machines [C]∥2011 International Conference on Power Engineering, Energy and Electrical Drives. Piscataway: IEEE, 2011: 1-6.
[15] ZHONG Q C, WEISS G. Synchronverters: inverters that mimic synchronous generators[J]. IEEE Transactions on Industrial Electronics, 2011, 58(4): 1259-1267.
[16] ALIPOOR J, MIURA Y, ISE T. Stability assessment and optimization methods for microgrid with multiple VSG units[ J]. IEEE Transactions on Smart Grid, 2018, 9(2): 1462-1471.
[17] 张亚楠, 朱淼, 张建文,等. 基于自适应调节的微源逆 变器虚拟同步发电机控制策略[J]. 电源学报, 2016, 14(3): 11-19. ZHANG Y N, ZHU M, ZHANG J W, et al. Control strategy of virtual synchronous generator based on adaptive adjusting for distributed inverters[ J]. Journal of Power Supply, 2016, 14(3): 11-19.
[18] MOLINA-GARCÍA A, BOUFFARD F, KIRSCHEND S. Decentralized demand-side contribution to primary frequency control [ J]. IEEE Transactions on Power Systems, 2011, 26(1): 411-419.
[19] 李俊, 任冲, 樊国旗,等. 基于模糊控制的高占比风电 系统自适应虚拟惯量及调频参数补偿策略研究[J]. 电力电容器与无功补偿, 2021, 42(4): 55-61. LI J, REN C, FAN G Q, et al. Study on adaptive virtual inertia and frequency modulation parameter compensation strategy of high proportion wind power system based on fuzzy control [ J]. Power Capacitor & Reactive Power Compensation, 2021, 42(4): 55-61.
[20] WANG W Y, JIANG L, CAO Y J, et al. A parameter alternating VSG controller of VSC-MTDC systems for low frequency oscillation damping[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4609-4621.
[21] 胡文强, 吴在军, 孙充勃, 等. 基于VSG 的储能系统 并网逆变器建模与参数整定方法[J]. 电力自动化设 备, 2018, 38(8): 13-23. HU W Q, WU Z J, SUN C B, et al. Modeling and parameter setting method for grid-connected inverter of energy storage system based on VSG[J]. Electric Power Automation Equipment, 2018, 38(8): 13-23.
[22] 丁小彬, 谢宇轩, 薛皓文,等. 基于神经网络算法的滚 刀磨损量预测方法[ J]. 郑州大学学报( 工学版), 2023, 44(1): 83-88, 95. DING X B, XIE Y X, XUE H W, et al. A method for disc cutter wear prediction based on neural network[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44(1): 83-88, 95.

备注/Memo

备注/Memo:
收稿日期:2023-10-07;修订日期:2023-11-15
基金项目:国家自然科学基金资助项目(62273312);河南省自然科学基金资助项目(212300410406)
作者简介:王明东(1971— ),男,河南台前人,郑州大学教授,博士,主要从事新型电力系统分析与控制方面的研究,E-mail:wangmingdong@ zzu. edu. cn。
更新日期/Last Update: 2024-04-29