摘要
随着新能源接入电力系统并通过直流送出,送端系统的暂态过电压问题逐渐突出。因此,为快速准确估计送端系统在直流闭锁、换相失败等预想扰动场景下各直流近区节点暂态过电压严重度,提出一种基于图卷积神经网络(graph convolutional network,GCN)的直流送端系统暂态过电压评估模型。该模型以电网发生直流故障前的潮流状态参数与网络拓扑作为输入特征,可以同时预估电网多个关键节点(如风电场汇集节点)的暂态过电压严重度。利用含跨区直流异步互联的两区域系统进行算例分析,验证该模型可以适应多种网架拓扑结构、不同新能源发电占比等差异化电网运行方式,具有较强的泛化能力。同时,所提模型揭示了对过电压严重度影响最大的关键因素,具有一定的可解释性,可为暂态过电压的预防控制提供有效指导。
As new energy sources are connected to the power system and sent out through DC,the transient overvoltage problem of the sending-end system is gradually becoming prominent.Therefore,a graph convolutional network(GCN)-based transient overvoltage evaluation model is proposed to quickly and accurately estimate the transient overvoltage severity at each DC near-zone node in expected disturbance scenarios such as DC block and commutation failure.This model takes the state parameters and the network topology before a DC fault occurs in the grid as input features,and can predict the transient overvoltage severity of multiple critical nodes of the grid(e.g.,wind farm aggregation node)simultaneously.A case study using a two-region system with cross-region DC asynchronous interconnection verifies that the model can be adapted to different grid operational modes,such as multiple grid topologies and different new energy generation ratios,and has a strong generalisability.At the same time,the proposed model reveals the key factors that have the greatest impact on overvoltage severity,and has a certain interpretability,which can provide effective guidance for the prevention and control of transient overvoltage.
作者
刘浩宇
刘挺坚
刘友波
丁理杰
史华勃
LIU Haoyu;LIU Tingjian;LIU Youbo;DING Lijie;SHI Huabo(College of Electrical Engineering,Sichuan University,Chengdu 610065,China;State Grid Sichuan Electric Power Research Institute,Chengdu 610000,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2023年第23期71-81,共11页
Power System Protection and Control
基金
国家自然科学基金项目资助(51977133)。
关键词
直流送端系统
闭锁
换相失败
暂态过电压
深度学习
图卷积神经网络
HVDC sending-end system
DC block
commutation failure
transient overvoltage
deep learning
graph convolutional neural network
