基于PCA和改进ABC-K聚类算法的双馈机组风电场等值建模*

doi:10.11985/2023.01.022

摘要/Abstract

摘要：

针对已有双馈风电机组风电场动态等值所建模型存在准确度不够高，适应性不够强的缺点，提出了一种基于主成分分析(Principal component analysis, PCA)的改进人工蜂群算法(Improve artificial bee colony, IABC)的动态等值建模方法。首先，对双馈风力机进行数学建模，获取机组的的所有状态参数；其次，利用主成分分析法将变量间的冗余性与相关性进行处理，提取发电机定转子q轴电流、电磁转矩、发电机转速四个主导变量作为分群指标，使用IABC搜索最佳聚类中心；最后，运用K-means算法对风电场进行聚类分群处理，并在Matlab/Simulink平台进行仿真。仿真结果表明本文所建等值模型在风速扰动和系统侧故障工况下都有较好的准确性和适应性。

关键词: 双馈风电机组, 主成分分析法, 人工蜂群算法, 主导变量, 等值建模

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

中图分类号:

TM561

张晓英, 吴宏强, 舒子江, 王琨, 陈伟. 基于PCA和改进ABC-K聚类算法的双馈机组风电场等值建模^*[J]. 电气工程学报, 2023, 18(1): 201-210.

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.

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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