Equivalent Modeling of DFIG Wind Farm Based on PCA and Improved ABC-K Clustering Algorithm

doi:10.11985/2023.01.022

Abstract

Abstract:

In view of the shortcomings of the existing doubly-fed induction generator(DFIG) wind farm dynamic equivalent model, such as the accuracy is not high enough and the adaptability is not strong enough, a dynamic equivalent modeling method based on principal component analysis(PCA) and improved artificial bee colony(IABC) is presented. Firstly, by modeling and analyzing the doubly-fed wind turbine, all the state variables that can represent the wind turbine are obtained. Secondly, principal component analysis is used to deal with the redundancy and correlation between variables, and four dominant variables, namely stator q-axis current, rotor q-axis current, electromagnetic torque and generator speed, are extracted as clustering indexes. IABC is used to search for the best clustering center. Finally, the K-means algorithm is used to cluster the wind farms, and the simulation is carried out on the Matlab/Simulink platform. The simulation results show that the equivalent model has good accuracy and adaptability under wind speed disturbance and system side fault conditions.

Key words: Doubly-fed wind generators(DFWGs), principal component analysis, artificial bee colony, dominant variable, equivalent modeling

CLC Number:

TM561

ZHANG Xiaoying, WU Hongqiang, SHU Zijiang, WANG Kun, CHEN Wei. Equivalent Modeling of DFIG Wind Farm Based on PCA and Improved ABC-K Clustering Algorithm[J]. Journal of Electrical Engineering, 2023, 18(1): 201-210.

Figures/Tables 22

References 21

[1]	王秀强. 朝阳之晖, 与时并明——2020年中国风电行业回顾与展望[J]. 能源, 2021(2):60-65.
	WANG Xiuqiang. The sunshine of the sun,keeping pace with the times:Review and prospect of China’s wind power industry in 2020[J]. Energy, 2021(2):60-65.
[2]	郭志, 陈洁, 黄净, 等. 基于改进遗传KM聚类算法的风电场机群划分方法[J]. 可再生能源, 2016, 34(2):238-243.
	GUO Zhi, CHEN Jie, HUANG Jing, et al. Wind farm cluster partition method based on improved genetic KM clustering algorithm[J]. Renewable Energy, 2016, 34(2):238-243.
[3]	LIU M, PAN W, ZHANG Y, et al. A dynamic equivalent model for DFIG-based wind farms[J]. IEEE Access, 2019, 7:74931-74940. doi: 10.1109/ACCESS.2019.2918359
[4]	吴红斌, 何叶, 赵波, 等. 基于改进K-means聚类算法的风电场动态等值[J]. 太阳能学报, 2018, 39(11):3232-3238.
	WU Hongbin, HE Ye, ZHAO Bo, et al. Dynamic equivalence of wind farms based on improved K-means clustering algorithm[J]. Acta Energiae Solaris Sinica, 2018, 39(11):3232-3238.
[5]	ZHOU Y, ZHAO L, MATSUO I B M, et al. A dynamic weighted aggregation equivalent modeling approach for the DFIG wind farm considering the Weibull distribution[C]// 2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS), May 05-08,2019,Calgary,AB,Canada. IEEE, 2019:1-7.
[6]	张元, 郝丽丽, 戴嘉祺. 风电场等值建模研究综述[J]. 电力系统保护与控制, 2015, 43(6):138-146.
	ZHANG Yuan, HAO Lili, DAI Jiaqi. Review of wind farm equivalent modeling[J]. Power System Protection and Control, 2015, 43(6):138-146.
[7]	李环平, 杨金明. 基于PSCAD_EMTDC的大型并网风电场建模与仿真[J]. 电力系统保护与控制, 2009, 37(21):62-66.
	LI Huanping, YANG Jinming. Model and simulation of large grid connected wind farm based on PSCAD_EMTDC[J]. Power System Protection and Control, 2009, 37(21):62-66.
[8]	MENG Z J. An improved equivalent wind method for the aggregation of DFIG wind turbines[C]// 2010 International Conference on Power System Technology, October 24-28,2010,Zhejiang,China. IEEE, 2010:1-6.
[9]	吴夙慧, 成颖, 郑彦宁, 等. K-means算法研究综述[J]. 现代图书情报技术, 2011(5):28-35.
	WU Suhui, CHENG Ying, ZHENG Yanning, el al. Review of K-means algorithm[J]. Modern Library and Information Technology, 2011(5):28-35.
[10]	王千, 王成, 冯振元. K-means聚类算法研究综述[J]. 电子设计工程, 2012, 20(7):21-24.
	WANG Qian, WANG Cheng, FENG Zhenyuan. Review of K-means clustering algorithm[J]. Electronic Design Engineering, 2012, 20(7):21-24.
[11]	王聪. 基于聚类算法的风电场动态等值问题研究[D]. 吉林: 东北电力大学, 2012.
	WANG Cong. Research on dynamic equivalence of wind farm based on clustering algorithm[D]. Jilin:Northeast Electric Power University, 2012.
[12]	严干贵, 李鸿博, 穆钢, 等. 基于等效风速的风电场等值建模[J]. 东北电力大学学报, 2011, 31(3):13-19.
	YAN Gangui, LI Hongbo, MU Gang, et al. Wind farm equivalent modeling based on equivalent wind speed[J]. Journal of Northeast Electric Power University, 2011, 31(3):13-19.
[13]	米增强, 苏勋文, 杨奇逊, 等. 风电场动态等值模型的多机表征方法[J]. 电工技术学报, 2010, 25(5):162-169.
	MI Zengqiang, SU Xunwen, YANG Qixun, et al. Multi machine characterization method of wind farm dynamic equivalent model[J]. Transactions of China Electrotechnical Society, 2010, 25(5):162-169.
[14]	米增强, 苏勋文, 余洋, 等. 双馈机组风电场动态等效模型研究[J]. 电力系统自动化, 2010, 34(17):72-77.
	MI Zengqiang, SU Xunwen, YU Yang, et al. Research on dynamic equivalent model of doubly fed wind farm[J]. Automation of Electric Power Systems, 2010, 34(17):72-77.
[15]	陈树勇, 王聪, 申洪, 等. 基于聚类算法的风电场动态等值[J]. 中国电机工程学报, 2012, 32(4):11-19,24.
	CHEN Shuyong, WANG Cong, SHEN Hong, et al. Dynamic equivalence of wind farms based on clustering algorithm[J]. Proceedings of the CSEE, 2012, 32(4):11-19,24.
[16]	GARCIA G M, COMECH M P, SALLAN J, et al. Modelling wind farms for grid disturbance studies[J]. Renewable Energy, 2008, 33(9):2109-2121. doi: 10.1016/j.renene.2007.12.007
[17]	颜湘武, 李君岩. 基于主成分分析法的直驱式风电场分群方法[J]. 电力系统保护与控制, 2020, 48(5):127-133.
	YAN Xiangwu, LI Junyan. Clustering method of direct drive wind farm based on principal component analysis[J]. Power System Protection and Control, 2020, 48(5):127-133.
[18]	韩平平, 夏雨, 丁明, 等. 基于PCA和CA-ST方法的风电场等值建模研究[J]. 太阳能学报, 2020, 41(11):267-277.
	HAN Pingping, XIA Yu, DING Ming, et al. Research on wind farm equivalent modeling based on PCA and CA-ST methods[J]. Acta Energiae Solaris Sinica, 2020, 41(11):267-277.
[19]	VASWANI N, NARAYANAMURTHY P. Static and dynamic robust PCA and matrix completion:A review[J]. Proceedings of the IEEE, 2018, 106(8):1359-1379. doi: 10.1109/JPROC.2018.2844126
[20]	喻金平, 郑杰, 梅宏标. 基于改进人工蜂群算法的K均值聚类算法[J]. 计算机应用, 2014, 34(4):1065-1069,1088. doi: 10.11772/j.issn.1001-9081.2014.04.1065
	YU Jinping, ZHENG Jie, MEI Hongbiao. K-means clustering algorithm based on improved artificial bee colony algorithm[J]. Journal of Computer Applications, 2014, 34(4):1065-1069,1088. doi: 10.11772/j.issn.1001-9081.2014.04.1065
[21]	WANG X, YU H, LIN Y, et al. Dynamic equivalent modeling for wind farms with DFIGs using the artificial bee colony with K-means algorithm[J]. IEEE Access, 2020, 8:173723-173731. doi: 10.1109/Access.6287639

特征值	贡献率	累积贡献率
9.733 8 1.002 9 0.261 5 0.001 5 0	0.884 8 0.091 1 0.023 7 0.000 1 0	0.884 8 0.975 9 0.999 6 1 1
0 0 0 0 0 0	0 0 0 0 0 0	1 1 1 1 1 1

特征变量		第二主成分的构成
ω_r	-0.254 1	0.599 1
T_M	0.317 1	0.092 3
T_T	0.317 1	0.092 3
T_e	0.317 1	0.092 3
i_sq	0.317 1	0.092 3
i_rd	0.289 3	0.320 9
i_rq	-0.317 2	-0.094 1
u_rd	-0.314 8	0.184 1
u_rq	-0.256 0	0.591 3
E_d E_q	0.317 0 0.289 3	0.091 7 0.320 9

数据集名称	样本个数	数据维数	类别组数
Iris	150	4	3
Wine	178	13	3
Glass	214	10	6

控制器	参数	数值
转子侧变流器	控制方式	恒无功控制
	电流内环PI控制[K_p, K_i]	[0.3 8]
	有功功率PI控制[K_p, K_i]	[1 100]
	无功功率PI控制[K_p, K_i] 动态无功电流比例系数	[0.05 5] 2.7
定子侧变流器	电流内环PI控制[K_p, K_i] 直流母线电压PI控制[K_p, K_i]		[1 100] [0.002 0.05]
	功率解锁电压值/p.u.	0.9
低电压有功限制	功率解锁对应电流值/p.u. 功率闭锁电压值/p.u.	1.22 0.4

设备及线路	参数	数值	参数	数值
发电机	额定功率/MW	1.5	额定电压/V	690
	额定频率/Hz	50	X_m/p.u.	2.9
	R_s/p.u.	0.001	X_s/p.u.	0.128
	R_r/p.u.	0.005	X_r/p.u.	0.156
机端变压器	S/(MV·A)	2	X_T/p.u.	25.5
主变压器	S/(MV·A)	50	X_T/p.u.	500
Crowbar	投入门槛电流/p.u.	1.5	旁路电阻/p.u.	0.1
机组线路	R₀/(Ω/km)	0.115	X₀/(Ω/km)	0.365
出口线路	R₁/(Ω/km)	0.107	X₁/(Ω/km)	0.414