广东电力2024，Vol.37Issue(6)：62-69,8.DOI:10.3969/j.issn.1007-290X.2024.06.007
基于深层级联残差图卷积的暂态稳定评估模型及其实际电网应用
Application of Transient Stability Assessment Model Based on Deep Cascading Residual Graph Convolution Network in Real World Power Grids
摘要
Abstract
The studies and tests of the data-driven transient power angle stability assessment models are mainly carried out in small-scale example systems,with insufficient application tests in real power grids.The internal reason is that the contradiction between the local information extraction characteristics and the global power angle stability in graph deep learning has not been solved.In this paper,a deep cascade residual map convolution model is designed to achieve an effective improvement in model layer stacking performance through a deep cascade structure containing residual connectivity.It also proposes the MinMaxPooling module to decouple the model parameters from the system size.The model structure design independent of the number of nodes can solve the problem of data-driven models applied to large-scale real power grids.The validity and performance of the proposed model are tested on a real regional power grid with 5 419 nodes.关键词
暂态功角稳定评估/数据驱动/图卷积模型/残差连接/深层级联/实际电网测试Key words
transient power angle stability assessment/data-driven/graph convolution network/residual connectivity/deep cascading/real grid testing分类
信息技术与安全科学引用本文复制引用
向川,陈鎏凯,陈勇,马遵,管霖..基于深层级联残差图卷积的暂态稳定评估模型及其实际电网应用[J].广东电力,2024,37(6):62-69,8.基金项目
云南电网有限责任公司科技项目(056200KK52220044) （056200KK52220044）
国家自然科学基金项目(52077080) （52077080）
