Optimization of DFIG Comprehensive Adaptive Frequency Regulation Parameters Based on Extreme Learning Machine

doi:10.16183/j.cnki.jsjtu.2021.S2.007

Abstract

Abstract:

Aiming at the problem of insufficient system frequency regulation ability caused by wind turbines connected to the grid, doubly fed induction generators (DFIG) mostly use virtual inertia and droop control to participate the frequency regualtion of the power system. However, traditional control strategies cannot fully utilize the frequency regulation capability of DFIG. In order to further improve the frequency stability of the system, the adaptive control of the virtual inertia and the droop coefficient are realized by analyzing the effects of the virtual inertia and the droop coefficient in each stage of the frequency dynamic response. Then, based on the extreme learning machine to predict the various frequency regulation index at different levels of wind speed, the objective function of the frequency regulation index is established to achieve the optimization of the comprehensive adaptive frequency regulation parameters, and the variable load shedding rate active standby control scheme adapted to the wind speed is proposed. The simulation results show the effectiveness of the method.

Key words: doubly fed induction generator (DFIG), extreme learning machine, integrated adaptive control, frequency dynamic response

CLC Number:

TM743

JIN Haochun, GE Minhui, XU Bo. Optimization of DFIG Comprehensive Adaptive Frequency Regulation Parameters Based on Extreme Learning Machine[J]. Journal of Shanghai Jiao Tong University, 2021, 55(S2): 42-50.

Figures/Tables 14

Fig.1

Fig.2

Tab.1

Fig.3

Fig.4

Fig.5

Tab.2

Tab.3

Tab.4

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

References 17

[1]	刘巨, 姚伟, 文劲宇, 等. 大规模风电参与系统频率调整的技术展望[J]. 电网技术, 2014, 38(3): 638-646.
	LIU Ju, YAO Wei, WEN Jinyu, et al. Prospect of technology for large scale wind farm participating into power grid frequency regulation[J]. Power System Technology, 2014, 38(3): 638-646.
[2]	邵昊舒, 蔡旭. 大型风电机组惯量控制研究现状与展望[J]. 上海交通大学学报, 2018, 52(10): 1166-1177.
	SHAO Haoshu, CAI Xu. Research status and prospect of inertia control for large scale wind turbines[J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1166-1177.
[3]	秦晓辉, 苏丽宁, 迟永宁, 等. 大电网中虚拟同步发电机惯量支撑与一次调频功能定位辨析[J]. 电力系统自动化, 2018, 42(09): 36-43.
	QIN Xiaohui, SU Lining, CHI Yongning, et al. Analysis of inertia support and primary frequency modulation function positioning of virtual synchronous generators in large power grids[J]. Automation of Electric Power Systems, 2018, 42(09): 36-43.
[4]	GEORGETA B, REMUS P, CRISTIAN P, et al. Spatial assessment of wind power potential at global scale A geographical approach[J]. Journal of Cleaner Production, 2018, 200(32): 1065-1086. doi: 10.1016/j.jclepro.2018.07.288 URL
[5]	李少林, 王伟胜, 张兴, 等. 风力发电对系统频率影响及虚拟惯量综合控制[J]. 电力系统自动化, 2019, 43(15): 64-70.
	LI Shaolin, WANG Weisheng, ZHANG Xing, et al. Impact of wind power on power system frequency and combined virtual inertia control[J]. Automation of Electric Power Systems, 2019, 43(15): 64-70.
[6]	LEE J, MULJADI E, SORENSEN P, et al. Releasable kinetic energy-based inertial control of a DFIG wind power plant[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1): 279-288. doi: 10.1109/TSTE.2015.2493165 URL
[7]	柴建云, 赵杨阳, 孙旭东, 等. 虚拟同步发电机技术在风发电系统中的应用与展望[J]. 电力系统自动化, 2018, 42(09): 17-25.
	CHAI Jianyun, ZHAO Yangyang, SUN Xudong, et al. Application and prospect of virtual synchronous generator technology in wind power generation system[J]. Automation of Electric Power Systems, 2018, 42(09): 17-25.
[8]	张冠锋, 杨俊友, 孙峰, 等. 基于虚拟惯量和频率下垂控制的双馈风电机组一次调频策略[J]. 电工技术学报, 2017, 32(22): 225-232.
	ZHANG Guanfeng, YANG Junyou, SUN Feng, et al. Primary frequency regulation strategy of DFIG based on virtual inertia and frequency droop control[J]. Transactions of China Electrotechnical Society, 2017, 32(22): 225-232.
[9]	邢鹏翔, 侍乔明, 王刚, 等. 风电机组虚拟惯量控制的响应特性及机理分析[J]. 高电压技术, 2018, 44(4): 1302-1310.
	XING Pengxiang, SHI Qiaoming, WANG Gang, et al. Response characteristics and mechanism analysis about virtual inertia control of wind generators[J]. High Voltage Engineering, 2018, 44(4): 1302-1310.
[10]	赵晶晶, 吕雪, 符杨, 等. 基于可变系数的双馈风机虚拟惯量与超速控制协调的风光柴微电网频率调节技术[J]. 电工技术学报, 2015, 30(5): 59-68.
	ZHAO Jingjing, LÜ Xue, FU Yang, et al. Wind-solar diesel microgrid frequency adjustment technology based on variable coefficient-based virtual inertia and overspeed control of doubly-fed wind turbines[J]. Transactions of China Electrotechnical Society, 2015, 30(5): 59-68.
[11]	LI D, ZHU Q, LIN S, et al. A self-adaptive inertia and damping combination control of VSG to support frequency stability[J]. IEEE Transactions on Energy Conversion, 2017, 32(1): 397-398. doi: 10.1109/TEC.2016.2623982 URL
[12]	程启明, 余德清, 程尹曼, 等. 基于自适应旋转惯量的虚拟同步发电机控制策略[J]. 电力自动化设备, 2018, 38(12): 79-85.
	CHENG Qiming, YU Deqing, CHENG Yinman, et al. Control strategy of virtual synchronous generator based on adaptive rotational inertia[J]. Electric Power Automation Equipment, 2018, 38(12): 79-85.
[13]	杨旭红, 姚凤军, 郝鹏飞, 等. 基于改进型RBF神经网络的VSG转动惯量自适应控制[J]. 电测与仪表, 2021, 58(2): 112-117.
	YANG Xuhong, YAO Fengjun, HAO Pengfei, et al. Adaptive control of VSG moment of inertia based on improved RBF neural network[J]. Electrical Measurement and Instrumentation, 2021, 58(2): 112-117.
[14]	颜湘武, 孙颖, 李晓宇, 等. 基于双馈风力发电场虚拟惯量控制策略优化[J]. 华北电力大学学报, 2020, 47(6): 42-51.
	YAN Xiangwu, SUN Ying, LI Xiaoyu, et al. Optimization of virtual inertia control strategy for doubly-fed wind farms[J]. Journal of North China Electric Power University, 2020, 47(6): 42-51.
[15]	兰飞, 潘益丰, 时萌, 等. 双馈风电机组变系数虚拟惯量优化控制[J]. 电力系统自动化, 2019, 43(12): 51-59.
	LAN Fei, PAN Yifeng, SHI Meng, et al. Variable coefficient virtual inertia optimal control for doubly-fed wind turbines[J]. Automation of Electric Power Systems, 2019, 43(12): 51-59.
[16]	HUANG G, SONG S J, GUPTA D. Semi-supervised and unsupervised extreme learning mchines[J]. IEEE Transactions on Cybernetics, 2014, 44(12): 2405-2417. doi: 10.1109/TCYB.2014.2307349 URL
[17]	张林林, 胡熊伟, 李鹏, 等. 基于极限学习机的电力系统暂态稳定评估方法[J]. 上海交通大学学报, 2019, 53(6): 749-756.
	ZHANG Linlin, HU Xiongwei, LI Peng, et al. Power system transient stability assessment based on extreme learning machine[J]. Journal of Shanghai Jiao Tong University, 2019, 53(6): 749-756.

区间	Δf	df/dt	符号	阶段
a-b	<0	<0	同号	偏离
b-c	<0	>0	异号	恢复
c-d	>0	>0	同号	偏离
d-e	>0	<0	异号	恢复

v/(m·s^-1)	K_p0	K_d0	k₁	k₂
8	0~20	0~5	0~70	0~70
10	0~40	0~10	0~70	0~70
12	0~50	0~20	0~70	0~70

v/ (m·s^-1)	Δf_max/ Hz	Δf_ste/ Hz	Δω_min (p.u.)	Δt_ste/s	Δt_ω/s
8	0.013	0.027	0.019	0.17	0.34
10	0.029	0.035	0.022	0.24	0.46
12	0.035	0.028	0.013	0.32	0.35