首页|Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix
Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnect-ed synchronous machines in power systems.The wide deploy-ment of phasor measurement units(PMUs)promotes the devel-opment 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 incorpo-rates 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 mea-surement 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 con-ditions has been proved.
School of Electrical Engineering,Xi'an Jiaotong Universi-ty,Xi'an 710049,China
School of Electrical Engineering,and the Center of Nanomaterials for Renewable Energy,State Key Laboratory of Electrical Insulation and Power Equipment,Xi'an Jiaotong Uni-versity,Xi'an 710049,China
Department of Computer Science and Engineering,The Chinese University of Hong Kong,Shatin,999077,Hong Kong,China
National Natural Science Foundation of ChinaHPC Platform,Xi'an Jiaotong University
NETL
NSTL
万方数据
