AUTHOR=Liu Jianfeng , Yao Chenxi , Chen Lele 

TITLE=Time-Adaptive Transient Stability Assessment Based on the Gating Spatiotemporal Graph Neural Network and Gated Recurrent Unit

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.885673

DOI=10.3389/fenrg.2022.885673

ISSN=2296-598X

ABSTRACT=With the continuous expansion of UHV AC / DC interconnection scale, on-line high-precision and fast transient stability assessment (TSA) is very important for the safe operation of power grid. In this paper, a TSA method based on gating spatial temporal graph neural network (GSTGNN) is proposed. The time adaptive method is used to improve the accuracy and speed of TSA. Firstly, in order to reduce the impact of dynamic topology on TSA after fault removal, GSTGNN is used to extract and fuse the key features of topology and attribute information of adjacent nodes to learn the spatial data correlation and improve the evaluation accuracy. Then, the extracted features are input into the gated recurrent unit (GRU) to learn the correlation of data at each time. Fast and accurate evaluation results are output from the stability threshold. At the same time, in order to avoid the influence of the quality of training samples, the improved weighted cross entropy loss function with K nearest neighbor (KNN) idea is used to deal with the unbalanced training samples. Through the analysis of an example, it is proved from the data visualization that TSA method can effectively improve assessment accuracy and shorten assessment time.