Transient Voltage Stability Assessment of Power System Based on GCN-LSTM
In order to improve the stability assessment of transient voltage of power system in case of topology varia-tion and enhance the feature extraction ability in spatiotemporal aspects,a method combining graph convolutional network and recurrent neural network is proposed.Firstly,a graph convolutional network is introduced to represent power data,modeling the power system as a network structure and automatically learning the feature representations of voltage nodes.Then,the recurrent neural network is proposed to handle the temporal dependencies of transient voltage data and capture the temporal characteristics of transient voltage data.After that,an adaptive enhancement module is proposed to fuse the two output feature representations with each other and improve the spatiotemporal fea-ture extraction ability of the model on the system topology structure.Finally,it is shown by numerical examples that the proposed method,compared to traditional assessment models,has higher prediction accuracy and effectiveness.
power systemtransient voltage stabilityrecurrent neural networkgraph convolutional network
万方数据
维普
