Transient Stability Assessment of Power Systems Built upon a Deep Spatio-Temporal Feature Extraction Network

Yu Nan, Meng Tong, Zhenzhen Kong, Huichao Zhao () and Yadong Zhao
Additional contact information
Yu Nan: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Meng Tong: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Zhenzhen Kong: State Grid Henan Electric Power Company Kaifeng Power Supply Company, Kaifeng 475000, China
Huichao Zhao: Department of Electrical Power Engineering, North China Electric Power University, Baoding 071000, China
Yadong Zhao: Department of Automation, North China Electric Power University, Baoding 071000, China

Energies, 2025, vol. 18, issue 17, 1-16

Abstract: The rapid and accurate identification of power system transient stability status is a fundamental prerequisite for ensuring the secure and reliable operation of large-scale power grids. With the increasing complexity and heterogeneity of modern power system components, system nonlinearity has grown significantly, rendering traditional time-domain simulation and direct methods unable to meet accuracy and efficiency requirements simultaneously. To further improve the prediction accuracy of power system transient stability and provide more refined assessment results, this paper integrates deep learning with power system transient stability and proposes a transient stability assessment of power systems built upon a deep spatio-temporal feature extraction network method. First, a spatio-temporal feature extraction module is constructed by combining an improved graph attention network with a residual bidirectional temporal convolutional network, aiming to capture the spatial and bidirectional temporal characteristics of transient stability data. Second, a classification module is developed using the Kolmogorov–Arnold network to establish the mapping relationship between spatio-temporal features and transient stability states. This enables the accurate determination of the system’s transient stability status within a short time after fault occurrence. Finally, a weighted cross-entropy loss function is employed to address the issue of low prediction accuracy caused by the imbalanced sample distribution in the evaluation model. The feasibility, effectiveness, and superiority of the proposed method are validated through tests on the New England 10-machine 39-bus system and the NPCC 48-machine 140-bus system.

Keywords: transient stability assessment; deep learning; spatial features; temporal features (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/17/4547/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/17/4547/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:17:p:4547-:d:1734111

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().