电力系统保护与控制2023，Vol.51Issue(23)：71-81,11.DOI:10.19783/j.cnki.pspc.230654
基于图卷积神经网络的直流送端系统暂态过电压评估
A method for evaluating transient overvoltage of an HVDC sending-end system based on a graph convolutional network
摘要
Abstract
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.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Key words
HVDC sending-end system/DC block/commutation failure/transient overvoltage/deep learning/graph convolutional neural network引用本文复制引用
刘浩宇,刘挺坚,刘友波,丁理杰,史华勃..基于图卷积神经网络的直流送端系统暂态过电压评估[J].电力系统保护与控制,2023,51(23):71-81,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.51977133).国家自然科学基金项目资助(51977133) （No.51977133）
