考虑密集通道的多机电力系统暂态稳定性评估方法

doi:10.11930/j.issn.1004-9649.202501042

摘要/Abstract

摘要：

为提高电力系统暂态稳定性评估的准确率和效率，提出了一种基于深度迁移学习的考虑密集通道的多机电力系统暂态稳定性评估方法。首先提出了一种新的暂态稳定性指标，建立了考虑密集通道的情况下电力系统的数据集及波动方程；然后提出了一种深度迁移学习方法，使用预训练的深度卷积神经网络进行暂态稳定性评估；最后通过仿真验证了所提方法具有较好的评估效率以及有效性。

关键词: 暂态稳定性评估, 深度迁移学习, 密集通道, 多机电力系统

Abstract:

To improve the accuracy and efficiency of power system transient stability assessment, a transient stability assessment method for multi-machine power system considering dense channel based on deep transfer is proposed. Firstly, a new transient stability index is proposed, and a data set and fluctuation equation of power system considering dense channel are established. Secondly, a deep transfer learning method for transient stability assessment using a pre-trained deep convolutional neural network is proposed. Finally, the effectiveness and efficiency of the proposed method are verified by simulation.

Key words: transient stability assessment, deep transfer learning, dense channels, multi-machine power systems

王学军, 方水平, 池光湧. 考虑密集通道的多机电力系统暂态稳定性评估方法[J]. 中国电力, 2025, 58(8): 139-146.

WANG Xuejun, FANG Shuiping, CHI Guangyong. Transient Stability Assessment Method for Multi-machine Power Systems Considering Dense Channels[J]. Electric Power, 2025, 58(8): 139-146.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202501042

https://www.electricpower.com.cn/CN/Y2025/V58/I8/139

图/表 6

图 1 文中方法流程

Fig.1 The method and process of this article

图 2 39节点系统模型

Fig.2 39 node system model

表 1 不同清除时间下的局部最大值和预测所需时间

Table 1 The designed electric power capacity of fan motors

清除时间/ms	角度指标		$ {V_{{\text{dV}}}} $指标
清除时间/ms	局部最大值/°	时间/s	局部最大值	时间/s
32	112	0.80	0.37	0.34
42	128	1.16	0.39	0.34
52	—	—	0.42	0.34

图 3 暂态稳定性评估结果

Fig.3 Transient stability assessment results

图 4 混淆矩阵分类比较

Fig.4 Comparison of confusion matrix classification

图 5 39节点系统暂态稳定性评估比较

Fig.5 Comparison of transient stability assessment of 39 node system

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