Transient Stability Assessment in Power Systems: A Spatiotemporal Graph Convolutional Network Approach with Graph Simplification

Dan Zhang, Yuan Yang (), Bingjie Shen, Tao Wang () and Min Cheng
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Dan Zhang: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
Yuan Yang: Hebei Key Laboratory of Physics and Energy Technology, Baoding 071003, China
Bingjie Shen: Hebei Key Laboratory of Physics and Energy Technology, Baoding 071003, China
Tao Wang: Hebei Key Laboratory of Physics and Energy Technology, Baoding 071003, China
Min Cheng: Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China

Energies, 2024, vol. 17, issue 20, 1-13

Abstract: Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable.

Keywords: transient stability assessment; graph simplification; spatial features; temporal features; sample imbalance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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