电力系统自动化2024，Vol.48Issue(13)：69-78,10.DOI:10.7500/AEPS20231031007
基于稳态特征量输入的大电网主导失稳机组辨识
Identification of Leading Instable Generators for Large-scale Power Grid Based on Steady-state Characteristic Inputs
摘要
Abstract
The data-driven stability assessment model with the input of steady-state characteristics has an important application prospect in the safety and stability research and judgment of new power systems,but it needs to solve the problem of extracting key characteristics caused by the large number of nodes and complex network structure in the model design,and provide more abundant assessment information such as instability modes.Therefore,a set of deep learning stability assessment model based on steady-state information input is designed for prediction of the leading instable generators of large-scale power grid.Firstly,a dynamic pooling dimensionality reduction model of heterogeneous graphs and node characteristics is proposed,which can dynamically merge nodes according to the similarity of node characteristics during the characteristic aggregation process to achieve parallel dimensionality reduction of large-scale power grid topology,node number and characteristics.Secondly,a generator-specified classifier model for the leading instable generators is proposed.Through global attention aggregation,the relative motion information of generators of the whole network is integrated into each generator characteristic vector,so that the identification model of leading instable generators can cope with the number of generator in structure and has good generalization ability.Finally,the model is verified in the actual large-scale power grid,and the effect and application performance of the key links are visually analyzed.关键词
深度学习/稳定评估/动态图池化/主导失稳机组Key words
deep learning/stability assessment/dynamic graph pooling/leading instability generator引用本文复制引用
虞景行,黄济宇,张勇军,钟康骅..基于稳态特征量输入的大电网主导失稳机组辨识[J].电力系统自动化,2024,48(13):69-78,10.基金项目
国家自然科学基金资助项目(52077080) （52077080）
广东省重点领域研发计划资助项目(2021B0101230001). This work is supported by National Natural Science Foundation of China(No.52077080)and Key Area R&D Program of Guangdong Province of China(No.2021B0101230001). （2021B0101230001）
