考虑新能源的暂态功角与电压稳定一体化评估

doi:10.11930/j.issn.1004-9649.202410057

中国电力 ›› 2025, Vol. 58 ›› Issue (6): 122-136.DOI: 10.11930/j.issn.1004-9649.202410057

考虑新能源的暂态功角与电压稳定一体化评估

步雨洛¹(), 吴俊勇²(), 史法顺³(), 季佳伸⁴()

1. 国网能源研究院有限公司，北京　102209
2. 北京交通大学电气工程学院，北京　100044
3. 清华大学电机工程与应用电子技术系，北京　100084
4. 国网北京市电力公司检修分公司，北京　100069

收稿日期:2024-10-18 发布日期:2025-06-30 出版日期:2025-06-28
作者简介:
步雨洛（2000），女，通信作者，硕士，从事人工智能、电力系统暂态稳定与控制、新能源发展研究，E-mail：b17231241@163.com
吴俊勇（1966），男，教授，博士生导师，从事人工智能、智能电网、电力系统分析、综合能源系统研究，E-mail：wujy@bjtu.edu.cn
史法顺（1997），男，助理研究员，从事人工智能、电力系统暂态稳定与控制研究，E-mail：shi_fashun@tsinghua.edu.cn
季佳伸（1998），男，硕士，从事人工智能、电力系统暂态电压稳定、高压直流输电技术研究，E-mail：970038639@qq.com
基金资助:
国家电网有限公司科技项目（4000-202257056A-1-1-ZN）。

Integrated Assessment of Transient Angle Stability and Voltage Stability Considering Renewable Energy Sources

BU Yuluo¹(), WU Junyong²(), SHI Fashun³(), JI Jiashen⁴()

1. State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
2. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
3. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
4. State Grid Beijing Maintenance Company, Beijing 100069, China

Received:2024-10-18 Online:2025-06-30 Published:2025-06-28
Supported by:
This work is supported by Science and Technology Project of SGCC (No.4000-202257056A-1-1-ZN).

摘要/Abstract

摘要：

暂态功角失稳与暂态电压失稳大多共同发生且相互影响，增加了稳定评估与紧急控制的难度。为实现稳定评估对紧急控制的全面指导性，提出了失稳模式识别方法。该方法以故障极限切除时间描述故障严重程度，通过功角失稳与电压失稳发生的先后标志主导性，以二者时间差描述耦合程度，构建了失稳模式识别四象限图。为实现在线的一体化评估，构建了基于融合卷积注意力机制模块（convolutional block attention module，CBAM）的改进卷积神经网络（convolutional neural networks，CNN）模型，提出了基于该模型的两阶段一体化稳定评估方案。最后，以新英格兰10机39节点系统为例进行仿真验证，结果表明该方法兼顾全面性、有效性及准确性；以含新能源的改进后10机39节点系统为例，说明所提方法在含新能源系统的适用性。

关键词: 暂态功角稳定, 暂态电压稳定, 卷积块注意力模块, 失稳模式, 卷积神经网络

Abstract:

Transient angle instability and transient voltage instability often occur simultaneously and interact with each other, which increases the difficulty of stability assessment and emergency control. To achieve comprehensive guidance for emergency control through stability assessment, an instability mode recongnition method is proposed. This method describes the fault severity using the critical fault clearing time, characterizes the dominance of angle instability and voltage instability based on their occurrence order, and quantifies the coupling degree with the time difference between them. A four-quadrant instability mode recongnition diagram is constructed. To achieve the integrated online assessment, an improved convolutional neural network (CNN) model based on the convolutional block attention module (CBAM) is developed, and a two-stage integrated stability assessment scheme is proposed based on this model. Finally, the New England 10-machine 39-bus system is used for simulation verification, and the results show that the proposed method can achieve comprehensiveness, effectiveness and accuracy. Further, the applicability of the proposed method in systems with renewable energy is demonstrated using a modified 10-machine 39-bus system incorporating renewable energy.

Key words: transient angle stability, transient voltage stability, convolutional block attention modul, instability mode, convolutional neural network.

步雨洛, 吴俊勇, 史法顺, 季佳伸. 考虑新能源的暂态功角与电压稳定一体化评估[J]. 中国电力, 2025, 58(6): 122-136.

BU Yuluo, WU Junyong, SHI Fashun, JI Jiashen. Integrated Assessment of Transient Angle Stability and Voltage Stability Considering Renewable Energy Sources[J]. Electric Power, 2025, 58(6): 122-136.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202410057

https://www.electricpower.com.cn/CN/Y2025/V58/I6/122

图/表 28

图 1 不同情况的扰动后轨迹

Fig.1 Post-disturbance trajectories of different cases

图 2 失稳模式识别四象限图

Fig.2 Four-quadrant diagram of instability mode recognition

图 3 IEEAC的实现流程

Fig.3 Implementation process of IEEAC

图 4 暂态功角失稳的2种曲线形态

Fig.4 Two types of curve patterns of transient angle instability

图 5 暂态电压失稳的2种曲线形态

Fig.5 Two types of curve patterns of transient voltage instability

图 6 失稳模式识别流程

Fig.6 Instability mode recognition flow

图 7 CBAM-CNN网络结构

Fig.7 Network structure of CBAM-CNN

图 8 两阶段智能评估流程

Fig.8 Two-stage intelligent assessment flow

表 1 时域仿真得到的数据集

Table 1 Data sets obtained by time domain simulation

编号	数据集	应用场景
1	δ_G V_B	第1阶段样本标注
2	δ_G P_eG P_eG s_M T_eM T_mM	第2阶段样本标注
3	V_B θ_B	模型训练

表 2 第1阶段混淆矩阵

Table 2 The first-stage confusion matrix

样本数	稳定(P)	失稳(P)
稳定(T)	N₁₁	N₁₂
失稳(T)	N₂₁	N₂₂

表 3 第2阶段混淆矩阵

Table 3 The second-stage confusion matrix

样本数	GT(P)	LS(P)	GT or LS(P)	GT and LS(P)
GT(T)	N₁₁	N₁₂	N₁₃	N₁₄
LS(T)	N₂₁	N₂₂	N₂₃	N₂₄
GT or LS(T)	N₃₁	N₃₂	N₃₃	N₃₄
GT and LS(T)	N₄₁	N₄₂	N₄₃	N₄₄

表 4 样本生成方案

Table 4 Configurations of the sample set generation

参数	具体设置	种类数
故障类型	三相短路故障	1
故障线路	交流线路（形成孤岛的线路除外）	33
故障位置	10%、30%、50%、70%、90%	5
负荷水平	80%、100%、120%	3
感应电动机占比	50%、60%、70%	3
故障持续时间	3-11周波	9

图 9 控制量比值可视化

Fig.9 Visualization of control volume ratios

图 10 新英格兰10机39节点紧急控制

Fig.10 Safety control of the New England 10-machine 39-bus system

图 11 系统故障严重程度

Fig.11 Schematic of the severity of system faults

图 12 系统主导强度

Fig.12 Schematic of system dominant intensity

表 5 4个实例的相关情况

Table 5 Relevant information of the four cases

编号	故障线路	主导强度	故障严重程度
A	31	–0.13	–0.055
B	24	0.12	–0.069
C	16	–0.04	–0.055
D	32	–0.17	–0.107

图 13 切机实例

Fig.13 Example of generator tripping (GT)

图 14 切负荷实例

Fig.14 Example of load shedding (LS)

图 15 切机或切负荷实例

Fig.15 Example of GT or LS

图 16 切机和切负荷同时动作实例

Fig.16 Example of simultaneous GT and LS

图 17 暂态功角与电压稳定评估混淆矩阵

Fig.17 Confusion matrix of TAS and TVS

表 6 第1阶段模型训练结果对比

Table 6 Comparison of the first-stage model training results

模型	功角评估		电压评估
模型	A₂/%	G_mean/%	A₂/%	G_mean/%
DT	94.72	94.67	94.88	94.82
MLP	95.60	95.81	95.29	94.94
GRU	97.05	97.08	96.97	96.95
RF	97.08	97.09	97.19	97.23
CNN	97.27	97.28	97.31	97.43
CBAM-CNN	97.83	97.87	97.68	97.65

图 18 第2阶段模型训练结果

Fig.18 The second-stage model training results

表 7 第2阶段模型训练结果对比

Table 7 Comparison of the second-stage model training results

模型	A₄/%	E_c/%
DT	91.27	6.05
MLP	92.14	5.11
GRU	93.28	4.52
RF	93.71	4.48
CNN	94.18	3.61
CBAM-CNN	95.13	2.28

图 19 模型鲁棒性测试结果

Fig.19 Results of model robustness test

表 8 改进后系统的第1阶段评估结果

Table 8 The first-stage assessment results of the improved system

算法	功角稳定		电压稳定
算法	A₂/%	G_mean/%	A₂/%	G_mean/%
CBAM-CNN	97.79	97.72	97.49	97.42

表 9 改进后系统的第2阶段评估结果

Table 9 The second-stage assessment results of the improved system

算法		A₄/%		E_c/%
CBAM-CNN		94.72		4.27

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考虑新能源的暂态功角与电压稳定一体化评估

Integrated Assessment of Transient Angle Stability and Voltage Stability Considering Renewable Energy Sources

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考虑新能源的暂态功角与电压稳定一体化评估

Integrated Assessment of Transient Angle Stability and Voltage Stability Considering Renewable Energy Sources

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摘要/Abstract

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参考文献 34

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