基于CBAM-CNN多任务模型的暂态电压稳定定量评估方法  

Quantitative Evaluation Method for Transient Voltage Stability Based on CBAM-CNN Multi-task Model

作　　者：容景超 毛晓明[1] 王炫 林权辉 杨炳鑫 RONG Jingchao;MAO Xiaoming;WANG Xuan;LIN Quanhui;YANG Bingxin(School of Automation,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学自动化学院,广州510006

出　　处：《高电压技术》2025年第1期281-289,共9页High Voltage Engineering

基　　金：广东省自然科学基金(2023A1515010716;2020B1515130001)。

摘　　要：为提升基于数据的暂态电压稳定评估的时效性并实现定量评估,提出一种基于卷积块注意力模块-卷积神经网络(convolutional block attention module-based convolutional neural network,CBAM-CNN)的暂态电压稳定评估模型。该模型以电网潮流数据、故障位置信息和节点电压突变信息为输入,引入混合注意力机制和多任务学习框架,输出电网各节点的暂态电压稳定水平和稳定标签。在经典IEEE 39节点系统中的研究表明,所提出的模型对潮流和故障位置变化具有充分的适应性;与其他几种常用的深度学习模型相比,所推荐的模型具有更强的信息表征能力和泛化能力,有望应用于电网预防控制策略的制订中。To improve the timeliness of data-based transient voltage stability assessment and to achieve quantitative evaluation,we proposed a transient voltage stability evaluation model based on convolutional block attention mod-ule-based convolutional neural network(CBAM-CNN).In the model,the power-flow,fault location,and bus voltage jump information are used as inputs,and hybrid attention mechanism and multi-task learning framework are introduced to output the transient voltage stability level and stability labels of each node in power grid.Studies in the classic IEEE 39-bus system show that the proposed model has sufficient adaptability to changes in power-flow and fault location;compared to several other popular deep-learning models,the recommended model has stronger information representation and generalization capabilities,and is expected to be applied in power system preventive controls.

关 键 词：暂态电压稳定 定量评估 多任务学习 注意力机制 卷积神经网络 深度学习 

分 类 号：TM712[电气工程—电力系统及自动化]