Open AccessMulti-task Transient Contingency Screening with Temporal Graph Convolutional Network in Power Systems
Author(s) -
Yinsheng Su,
Jiyu Huang,
Haicheng Yao,
Lin Guan,
Mengxuan Guo,
Zhi Zhong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2095/1/012027
Subject(s) - computer science , network topology , graph , contingency , convolution (computer science) , transient (computer programming) , topology (electrical circuits) , theoretical computer science , distributed computing , real time computing , artificial intelligence , engineering , computer network , philosophy , linguistics , artificial neural network , electrical engineering , operating system
Rapid transient stability assessment (TSA) is an essential requirement for power system security. In real-world applications, transient contingency screening (TCS) applies TSA approaches to address the pre-defined contingency sets under online operation conditions. TSA by time domain simulation (TDS) is time-consuming, hence we propose a high-speed temporal graph convolutional network (TGCN) that achieves TSA decisions such that a large-scale contingency set can be scanned quickly with enough precision. Based on multi-graph inputs to reflect the transient process, the TGCN utilizes the graph convolutional network (GCN) to extract topology representations and temporal convolution (TC) layers to encode temporal relations. After above graph embedding, two downstream networks are designed for stability classification and critical generator prediction, respectively. Test results on IEEE 39 Bus system demonstrate its superiority over existing models under different operation topologies, fault locations and clearing modes.