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

doi:10.11930/j.issn.1004-9649.202410057

Abstract

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.

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202410057

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

Figures/Tables 28

Fig.1 Post-disturbance trajectories of different cases

Fig.2 Four-quadrant diagram of instability mode recognition

Fig.3 Implementation process of IEEAC

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

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

Fig.6 Instability mode recognition flow

Fig.7 Network structure of CBAM-CNN

Fig.8 Two-stage intelligent assessment flow

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

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

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

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

Fig.9 Visualization of control volume ratios

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

Fig.11 Schematic of the severity of system faults

Fig.12 Schematic of system dominant intensity

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

Fig.13 Example of generator tripping (GT)

Fig.14 Example of load shedding (LS)

Fig.15 Example of GT or LS

Fig.16 Example of simultaneous GT and LS

Fig.17 Confusion matrix of TAS and TVS

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

Fig.18 The second-stage model training results

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

Fig.19 Results of model robustness test

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

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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