Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix  

作　　者：Peiyuan Sun Long Huo Xin Chen Siyuan Liang 

机构地区：[1]the School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China [2]the School of Electrical Engineering,and the Center of Nanomaterials for Renewable Energy,State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an 710049,China [3]the Department of Computer Science and Engineering,The Chinese University of Hong Kong,Shatin,999077,Hong Kong,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第3期695-706,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China(No.21773182);the HPC Platform,Xi’an Jiaotong University。

摘　　要：Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems.The wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS prediction.This paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data.The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid.Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered.Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN model.Results show that the TTEDNN model performs best among existing deep learning models.Furthermore,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关 键 词：Rotor angle stability prediction topological embedding deep learning graph convolution network 

分 类 号：TM73[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]