适应风电场宽频振荡研究的模糊度量层次聚类法*

doi:10.11985/2024.01.028

电气工程学报 ›› 2024, Vol. 19 ›› Issue (1): 261-271.doi: 10.11985/2024.01.028

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适应风电场宽频振荡研究的模糊度量层次聚类法^*

宋锐¹(), 甘嘉田¹(), 王泽嘉²(), 许德操¹(), 白左霞¹(), 韩民晓²()

1.国网青海省电力公司西宁 810008
2.华北电力大学电气与电子工程学院北京 102206

收稿日期:2023-01-20 修回日期:2023-08-30 出版日期:2024-03-25 发布日期:2024-04-25
通讯作者: 韩民晓，男，1963年生，博士，教授。主要研究方向为电力电子技术在电力系统中的应用。E-mail：hanminxiao@ncepu.edu.cn
作者简介:宋锐，男，1979年生。主要研究方向为电力系统分析。E-mail：sr3737@163.com;
甘嘉田，女，1995年生。主要研究方向为新能源并网技术。E-mail：13997230847@163.com;
王泽嘉，男，1998年生，硕士研究生。主要研究方向为新能源并网稳定性。E-mail：jiplant@163.com;
许德操，男，1981年生，硕士，高级工程师。主要研究方向为电网规划、设计和新能源消纳。E-mail：xudecao@126.com;
白左霞，女，1990年生，硕士，高级工程师。主要研究方向为电网规划、设计和新能源消纳。E-mail：bzxia0024@qh.sgcc.com.cn
基金资助:
国家电网公司科技资助项目(SGQHDKYODWJS2100138)

Fuzzy Metric Hierarchical Clustering Method for Wind Farm Wideband Oscillation Research

SONG Rui¹(), GAN Jiatian¹(), WANG Zejia²(), XU Decao¹(), BAI Zuoxia¹(), HAN Minxiao²()

1. State Grid Qinghai Electric Power Company, Xining 810008
2. School of Electrical and Electronics Engineering, North China Electric Power University, Beijing 102206

Received:2023-01-20 Revised:2023-08-30 Online:2024-03-25 Published:2024-04-25

摘要/Abstract

摘要：

为助力“双碳”目标实现，推动风电产业发展，研究并治理风电场并网宽频振荡问题迫在眉睫。为建立适用于风电场振荡研究且能准确高效反映实际风电场输出特性的仿真模型，提出基于模糊度量的层次聚类法，改善传统综合层次聚类算法使用距离度量造成的聚类结果偏移。利用F检验法定量评价聚类效果，横向比较F评分确定最优聚类方案，纵向比较确定最优聚类分群数。评价结果表明，模糊度量层次聚类法的聚类效果良好。最后采用PSCAD结合青海省海南新能源基地风电场实例进行了仿真分析，验证了模糊度量层次聚类法对风电场聚类的适用性。

关键词: 风电场, 宽频振荡, 模糊度量层次聚类, F检验, 聚类评价

Abstract:

On account of the pressing goal of “carbon peaking and carbon neutrality”, it is extremely urgent to develop wind power and study the broadband oscillation problem of wind farm connected to the grid. With the purpose of establishing a simulation model suitable for wind farm oscillation research, which is able to accurately and efficiently reflect the actual wind farm output characteristics, a fuzzy hierarchical clustering method is proposed that improves the clustering result offset caused by distance measurement in traditional balanced iterative reducing and clustering using hierarchies. Besides, F test is used to evaluate the clustering effect quantitatively, and F score can be used to horizontally compare to determine the optimal cluster number and longitudinal comparison to determine the optimal clustering scheme. Compared with traditional clustering method, fuzzy metric hierarchical clustering method has the highest F score. Finally, a wind farm simulation example consisting of 25 wind turbines with actual parameters in PSCAD is set up, and the conclusion is obtained that the most suitable clustering method for wind farms is fuzzy metric hierarchical clustering method.

Key words: Wind farm, broadband oscillation, fuzzy metric hierarchical clustering, F test, clustering evaluation

中图分类号:

TM614

宋锐, 甘嘉田, 王泽嘉, 许德操, 白左霞, 韩民晓. 适应风电场宽频振荡研究的模糊度量层次聚类法^*[J]. 电气工程学报, 2024, 19(1): 261-271.

SONG Rui, GAN Jiatian, WANG Zejia, XU Decao, BAI Zuoxia, HAN Minxiao. Fuzzy Metric Hierarchical Clustering Method for Wind Farm Wideband Oscillation Research[J]. Journal of Electrical Engineering, 2024, 19(1): 261-271.

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电机参数	取值	控制参数	取值
额定容量S/(MV·A)	2	RSC内环PI	1.2+0.025/s
额定线电压U/V	690	RSC外环PI	2+0.02/0.05(P/Q)/s
额定频率f/Hz	50	RSC PLL PI	2.2+0.238/s
定/转子变比	0.85	GSC内环PI	1+0.01/s
定子电阻R_s/p.u.	0.005 4	GSC外环PI	0.4+0.01(U_dc)/s
转子电阻R_r/p.u.	0.006 07	GSC PLL PI	1.2+0.05/s
励磁互感L_m/p.u.	4.5	直流电容C/p.u.	4.396
定子漏感L_1s/p.u.	0.099 8	滤波器电感L_f/p.u.	1.524×10^-6
转子漏感L_1r/p.u.	0.109 8	滤波器电容C_f/p.u.	0.061 544
电机漏磁系数σ	0.045	变压器变比N1/2/3	35/0.69/0.69

机组编号	风速/(m/s)	有功功率/MW	无功功率/MVar	出口电流/kA
1	8.33	1.198	0.005 80	0.018 5
2	8.53	1.25	0.005 65	0.019 6
3	12.7	2.001	0.002 73	0.032 2
4	10.87	2.012	0.000 70	0.032 0
5	8.71	1.278	0.001 30	0.021 0
6	11.3	2.008	0.000 36	0.032 3
7	9.21	1.476	0.001 39	0.023 7
8	12.88	2.003	0.004 26	0.032 2
9	9.47	1.565	0.001 34	0.024 7
10	9.64	1.624	0.005 13	0.026 0
11	9.84	1.702	0.001 53	0.027 2
12	9.9	1.722	0.009 29	0.028 4
13	13.11	2.005	0.000 72	0.032 0
14	13.45	2.005	0.005 26	0.032 1
15	13.56	2.001	0.007 97	0.032 0
16	11.28	2.001	0.006 06	0.032 3
17	10.2	1.86	0.006 83	0.030 6
18	10.51	2.006	0.006 11	0.032 8
19	10.63	2.017	0.009 80	0.032 8
20	10.8	2.003	0.006 09	0.032 7
21	11.2	2.004	0.006 79	0.032 0
22	11.45	2.004	0.000 65	0.032 1
23	11.52	2.005	0.001 34	0.032 1
24	12.1	1.991	0.006 23	0.032 8
25	14.23	1.999	0.004 88	0.032 8

簇编号	风机聚类结果	中心机组特性[V, P, Q, I]
①	{3, 4, 6, 8, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25}	[11.974, 2.004, 0.043 7, 0.032 3]
②	{10, 11, 12, 17}		[9.895, 1.727, 0.056 9, 0.028 0]
③	{1, 2, 5, 7, 9}		[8.85, 1.353, 0.030 9, 0.021 5]

特性	自由度	均方差	F平分	P(P<0.001时显著)
风速	2	1.117	108.43	<0.001
有功	2	1.240	140.972	<0.001
无功	2	0.085	0.897	0.422
电流	2	1.115	121.948	<0.001
多变量检验结果：三簇聚类F评分为62.284

簇编号	风机聚类结果	中心机组特性[V, P, Q, I]
①	{8, 12, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25}	[11.9, 2.004, 0.005 37, 0.032 4]
②	{3, 4, 6, 13, 22, 23}	[12.195, 2.004, 0.013 6, 0.032 1]
③	{10, 11, 12, 17}	[9.895, 1.727, 0.056 9, 0.028 0]
④	{1, 2, 5, 7, 9}	[8.85, 1.353, 0.030 9, 0.021 5]

适应风电场宽频振荡研究的模糊度量层次聚类法^*

Fuzzy Metric Hierarchical Clustering Method for Wind Farm Wideband Oscillation Research

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参考文献 21

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特性	自由度	均方差	F	P(P<0.001时显著)
风速	3	0.745	69.001	<0.001
有功	3	0.826	89.732	<0.001
无功	3	0.445	10.231	<0.001
电流	3	0.744	78.469	<0.001
多变量检验结果：四簇聚类F评分为63.928

聚类法	F检测评分	显著性
K-Means	59.194	<0.001
Meanshift	58.775	<0.001
BIRCH(距离度量)	54.983	<0.001
模糊度量层次聚类法	62.284	<0.001

风电场算例	输出特性			平均误差
风电场算例	有功功率/ MW	无功功率/ MVar	输出电流/ kA	平均误差
原算例	45.74	0.108 186	0.736 76	—
模糊度量层次聚类	45.304	0.131	0.753	0.198
K-Means	45.334	0.167	0.76	0.566
Meanshift	44.083	0.196	0.748	0.791
BIRCH(距离度量)	44.244	0.222	0.753	1.041

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