A graph attention networks-based model to distinguish the transient rotor angle instability and short-term voltage instability in power systems. (May 2022)

• Record Type: Journal Article
• Authors: Zhang, Runfeng; Yao, Wei; Shi, Zhongtuo; Zeng, Lingkang; Tang, Yong; Wen, Jinyu
• Is Part Of: International journal of electrical power & energy systems. Volume 137(2022)
• Publication Date: 2022-05
• Subjects: Digital simulation -- Simulation data analysis -- Dominant instability mode identification -- Graph convolutional networks -- Attention mechanism -- Label-smoothing; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107783
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: Digital simulation is significant for the operating mode and control decision-making of power systems. In the process of simulation data analysis, stability analysis is an essential part. One of the most challenging tasks is to distinguish between transient rotor angle instability and short-term voltage instability. This paper proposes a graph attention networks (GATs)-based method to overcome this ticklish problem via integrating power grid topology information into the neural networks. Compared with the conventional graph convolutional networks (GCNs), the attention mechanism is introduced into the GATs to learn the weights among different neighbor vertices in the graph. Due to the difficulty of distinguishing between the rotor angle instability and voltage instability in some samples, a label-smoothing method is adopted to alleviate the influence caused by label inaccuracy. Case studies are conducted on an 8-machine 36-bus system and Northeast China Power System. Simulation results show that the proposed method has better performance than conventional GCNs and other machine learning methods. Graphical abstract: Highlights: A deep learning-based large-scale power grid simulation analysis model is provided. A graph attention network (GAT) integrating power grid topology is proposed. The proposed label-smoothing loss function can tolerate label inaccuracy. The proposed GAT model can efficiently identify the dominant instability mode.