基于SCSC-Swin Transformer的电力系统暂态稳定预测方法  

Power system transient stability prediction method based onSCSC-Swin Transformer

作　　者：刘文胜 荣娜[1] 李宏伟 周洪才 张异浩 Liu Wensheng;Rong Na;Li Hongwei;Zhou Hongcai;Zhang Yihao(Department of Electrical Engineering,Guizhou University,Guiyang 550025,China;Tianshengqiao Bureau,CSG EHV Power Transmission Company,Xingyi 562400,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025 [2]南方电网超高压输电公司天生桥局,兴义562400

出　　处：《电子测量技术》2024年第22期120-128,共9页Electronic Measurement Technology

基　　金：贵州省科技支撑计划项目([2023]290);贵州省科技支撑计划项目([2023]329);贵州省科学技术基金(黔科合基础-ZK[2021]一般277);贵州省优秀青年科技人才项目(黔科合平台人才[2021]5645)资助。

摘　　要：现代电力系统在遭受扰动后,失稳模式呈现多样化,迫切需要准确识别不同的失稳模式,以采取相应的控制措施避免造成较大损失,因此本文提出一种基于改进Swin Transformer的电力系统暂态稳定评估方法。首先,通过时域仿真采集电力系统受扰后的电压幅值及相角特征构建起特征矩阵;然后,基于Swin Transformer,本文提出一种空间跨尺度卷积注意力模块,用来替代原来的多头自注意力模块,该模块通过一系列不同卷积核大小的卷积层,能够充分提取到不同维度的有效特征,进而实现更为准确的预测结果。最后,通过在修改后的New England 10机39节点系统及IEEE 50机145节点系统中进行仿真实验,预测准确率分别达到99.05%和99.00%,多摆失稳误判率为0.35%和0.27%,这表明所提方法不仅能够对不同的失稳模式进行准确的预测,同时在噪声及PMU特征缺失情况下仍表现出优越的鲁棒性。In modern power systems,instability modes have become increasingly diversified following disturbances,necessitating the accurate identification of various instability modes to implement appropriate control measures and prevent significant losses.Therefore,a transient stability assessment method for power systems based on an improved Swin Transformer is proposed in this paper.Firstly,time-domain simulations are conducted to collect voltage magnitude and phase angle characteristics following disturbances,which are used to construct a feature matrix.Then,building upon the Swin Transformer,a spatial cross-scale convolutional attention module is introduced to replace the original multi-head self-attention module.This new module utilizes a series of convolutional layers with different kernel sizes to effectively extract features across multiple dimensions,leading to more accurate prediction results.Finally,simulation experiments on the modified New England 10-machine 39-bus system and IEEE 50-machine 145-bus system show prediction accuracies of 99.05%and 99.00%,respectively,with multi-swing instability misjudgment rates of 0.35%and 0.27%.These results demonstrate that the proposed method not only accurately predicts different instability modes but also exhibits superior robustness in the presence of noise and missing PMU features.

关 键 词：暂态稳定预测 空间跨尺度卷积模块 失稳模式 Swin Transformer 数据驱动 

分 类 号：TM712[电气工程—电力系统及自动化] TN929.5[电子电信—通信与信息系统]