构建新型电力系统背景下的微电网鲁棒简化建模

doi:10.11930/j.issn.1004-9649.202307072

中国电力 ›› 2024, Vol. 57 ›› Issue (1): 148-157.DOI: 10.11930/j.issn.1004-9649.202307072

• 新型电力系统低碳规划与运行 • 上一篇下一篇

构建新型电力系统背景下的微电网鲁棒简化建模

王大兴¹(), 宁妍²(), 汪敬培³(), 徐洋⁴(), 毕峻⁵, 周铭标⁶, 王鹏⁴()

1. 国网四川省电力公司电力科学研究院，四川成都　610041
2. 浙江大有实业有限公司，浙江杭州　310009
3. 国网衢州供电公司，浙江衢州　324000
4. 电子科技大学机械与电气工程学院，四川成都　611731
5. 国网阿坝供电公司，四川阿坝　624000
6. 国网三明供电公司，福建三明　365000

收稿日期:2023-07-19 出版日期:2024-01-28 发布日期:2024-01-23
作者简介:王大兴（1984—），男，高级工程师，从事电网设备状态评价及运检技术研究，E-mail：wangdxsepri@163.com
宁妍（1984—），女，硕士，从事电力经济、配电网设计研究，E-mail：59977347@qq.com
汪敬培（1988—），男，高级工程师，从事电力工程、电力供需研究，E-mail：136599677@qq.com
徐洋（2000—），男，硕士研究生，从事电力系统运行与控制、电力系统建模研究，E-mail：xxuyoung@163.com
王鹏（1988—），男，通信作者，副教授，从事电力系统动态等值建模研究，E-mail：pengwang@uestc.edu.cn
基金资助:
国家自然科学基金资助项目（考虑不确定性和模型泛化能力的有源配电网动态等值建模方法研究，52007024）。

Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems

Daxing WANG¹(), Yan Ning²(), Jingpei WANG³(), Yang XU⁴(), Jun BI⁵, Mingbiao ZHOU⁶, Peng WANG⁴()

1. State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China
2. Zhejiang Dayou Industry Co., Ltd., Hangzhou 310009, China
3. State Grid Quzhou Power Supply Company, Quzhou 324000, China
4. School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China, Chengdu 611731, China
5. State Grid Aba Power Supply Company, Aba 624000, China
6. State Grid Sanming Electric Power Co., Ltd., Sanming 365000, China

Received:2023-07-19 Online:2024-01-28 Published:2024-01-23
Supported by:
This work is supported by National Natural Science Foundation of China (Dynamic Equivalent Modeling Method of Active Distribution Network Considering Uncertainty and Model Generalization Ability, No.52007024).

摘要/Abstract

摘要：

发展高比例可再生能源接入的微电网是构建新型电力系统，实现中国能源安全和低碳发展的重要手段。在分析微电网所接入系统的动态特征时，现有等值模型存在鲁棒性不强的问题，即等值模型虽然可以很好地复现真实系统在训练故障下的动态特征，但却无法准确反映系统在未知故障（非训练故障）下的真实响应。为此，首先采用k-means++对微电网的典型运行方式进行有效区分，以表征系统的随机性和时变性特征；其次，采用基于关键参数筛选的参数辨识方法，避免了参数辨识过程中的多解问题；然后，针对系统不同典型运行方式，利用卷积神经网络对等值模型参数进行泛化；最后，基于Fisher判别准则实现了等值模型参数的在线匹配，并在某实际微电网模型中验证了所提方法的有效性。

关键词: 微电网, 等值建模, 鲁棒性, k-means++聚类, 卷积神经网络, Fisher判别准则

Abstract:

The development of microgrid with high proportion of renewable energy is one of the important means to construct new modern power systems so as to achieve energy security and low carbon emissions. However, amid the analysis of the dynamic characteristics of microgrid-integrated power system, the current equivalent models appear to be not robust enough. Specifically, these models can well reproduce the behaviors of actual system under the faults in training set, they may not be able to reflect actual system responses under other unknown faults (non-training faults). In regard to this, k-means++ is introduced first to effectively distinguish the typical operation condition of microgrid such that the randomness and time-varying characteristics of the system can be represented. Next, key parameter selection-based parameter identification method is applied to avoid the issue of multiple solutions in parameter identification process. Then, the convolutional neural network is used to generalize the model parameters with respect to different typical system operation conditions. Additionally, online matching of equivalent model parameters is achieved by virtue of Fisher discriminant analysis. Finally, the effectiveness of the proposed method has been verified in a real microgrid system in China.

Key words: microgrid, equivalent modeling, robustness, k-means++ clustering, convolutional neural network, Fisher discriminant analysis

王大兴, 宁妍, 汪敬培, 徐洋, 毕峻, 周铭标, 王鹏. 构建新型电力系统背景下的微电网鲁棒简化建模[J]. 中国电力, 2024, 57(1): 148-157.

Daxing WANG, Yan Ning, Jingpei WANG, Yang XU, Jun BI, Mingbiao ZHOU, Peng WANG. Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems[J]. Electric Power, 2024, 57(1): 148-157.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307072

https://www.electricpower.com.cn/CN/Y2024/V57/I1/148

图/表 14

图 1 微电网等值模型

Fig.1 Equivalent model of active distribution network

图 2 同步发电机调速器模型

Fig.2 Governor control model of the synchronous generator

图 3 同步发电机励磁系统

Fig.3 Excitation system model of the synchronous generator

图 4 VSC控制策略

Fig.4 VSC control strategy

图 5 微电网测试系统

Fig.5 Microgrid test system

表 1 同步发电机及电压源型变流器参数灵敏度值

Table 1 Sensitivities of synchronous machine and VSC parameters

参数	灵敏度	参数	灵敏度	参数	灵敏度	参数	灵敏度
X_d	0.1231	K_q	0.0010	$X'_q $	0.4468	$X'_{q0} $	0.1342
$X'_d $	0.4201	T_pc	0.2034	$X''_q $	0.2834	$X''_{q0} $	0.0934
$X''_d $	0.1521	T₀	0.0236	$T'_{d0} $	0.0961	H	0.0128
X_q	0.7662	K_d	0.3741	$T''_{d0} $	0.0263	D	0.8341
K_p	0.6613	$ \varepsilon $	0.3127	T_q	0.002	K_qc	0.0026
T_p	0.4115	T_s	0.0357	K_pc	0.3701	T_qc	0.0001
T_r	0.1096	T₁	0.6236	T₂	0.2413	T₃	0.2123
T₄	0.1918	K_a	0.6621	K	0.7122	T_a	0.0490
K_c	0.0062

表 2 等值模型待辨识关键参数

Table 2 Identified key parameters of equivalent model

模型	参数
同步电机本体	D	X_q	$X'_q $	$X'_d $
励磁系统	K	K_a	T₁
调速系统	K_d	$ \varepsilon $
电压源型变流器	K_p	T_p	K_pc
负荷以及并联阻抗	P_eq	Q_eq	R_eq	X_eq

图 6 特征数据集聚类结果

Fig.6 The clustering result of feature dataset

表 3 特征数据集聚类结果

Table 3 The clustering results of characteristic data set

聚类组别	特征数据集个数	聚类组别	特征数据集个数	聚类组别	特征数据集个数
1	362	4	57	7	64
2	53	5	78
3	245	6	41

图 7 典型案例1系统公共连接点处有功功率曲线

Fig.7 Active power curves at PCC of typical scenario 1

图 8 典型案例2系统公共连接点处有功功率曲线

Fig.8 Active power curves at PCC of typical scenario 2

图 9 典型案例3系统公共连接点处有功功率曲线

Fig.9 Active power curves at PCC of typical scenario 3

图 10 典型案例4系统公共连接点处有功功率曲线

Fig.10 Active power curves at PCC of typical scenario 4

图 11 典型案例5系统公共连接点处有功功率曲线

Fig.11 Active power curves at PCC of typical scenario 5

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构建新型电力系统背景下的微电网鲁棒简化建模

Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems

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构建新型电力系统背景下的微电网鲁棒简化建模

Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems

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