Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability

Liukai Chen; Lin Guan

doi:10.1186/s41601-023-00278-x

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Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability

Original Research | 更新时间：2026-09-30

- Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability
- Static information, K-neighbor, and self-attention aggregated scheme: a transient stability prediction model with enhanced interpretability
- 现代电力系统保护与控制（英文） 2023年8卷第1期页码：1-16
- 作者机构：
  
  School of Electric Power, South China University of Technology,Guangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-023-00278-x
  中图分类号：
- 纸质出版：2023-01-01
- 稿件说明：
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Liukai Chen, Lin Guan. Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability[J]. 现代电力系统保护与控制（英文）, 2023,8(1):1-16.

Liukai Chen, Lin Guan. Static information, K-neighbor, and self-attention aggregated scheme: a transient stability prediction model with enhanced interpretability[J]. Protection and Control of Modern Power Systems, 2023, 8(1): 1-16.
Liukai Chen, Lin Guan. Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability[J]. 现代电力系统保护与控制（英文）, 2023,8(1):1-16. DOI： 10.1186/s41601-023-00278-x.

Liukai Chen, Lin Guan. Static information, K-neighbor, and self-attention aggregated scheme: a transient stability prediction model with enhanced interpretability[J]. Protection and Control of Modern Power Systems, 2023, 8(1): 1-16. DOI： 10.1186/s41601-023-00278-x.

摘要

Data-driven preventive scanning for transient stability assessment (DTSA) is a faster and more efficient solution than time-domain simulation (TDS). However

most current methods cannot balance generalization to different topologies and interpretability

with simple output. A model that conforms to the physical mechanism and richer label for transient stability can increase confidence in DTSA. Thus a static-information

k-neighbor

and self-attention aggregated schema (SKETCH) is proposed in this paper. Taking only static measurements as input

SKETCH gives several explanations that are consistent with the physical mechanisms of TSA and provides results for all generator stability while predicting system stability. A module based on the self-attention mechanism is designed to solve the locality problem of a graph neural network (GNN)

achieving subgraph equivalence outside the k-order neighborhood. Test re sults on the IEEE 39-bus system and IEEE 300-bus system indicate the superiority of SKETCH and also demonstrate the rich sample interpretation results. Highlights •A fast TSA scheme for pre-failure scanning. •A physical mechanism-based attention structure for dynamic graph pooling. •A node regression model that responds to key physical mechanisms. •Generator label for richer output information. •Top performance and post-hoc interpretation.

Abstract

Data-driven preventive scanning for transient stability assessment (DTSA) is a faster and more efficient solution than time-domain simulation (TDS). However

most current methods cannot balance generalization to different topologies and interpretability

with simple output. A model that conforms to the physical mechanism and richer label for transient stability can increase confidence in DTSA. Thus a static-information

k-neighbor

and self-attention aggregated schema (SKETCH) is proposed in this paper. Taking only static measurements as input

关键词

Keywords

references

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