Transient Stability Assessment of Power System Based on Spatio-Temporal Graph Convolutional Network

Yingrui ZHUANG; Tannan XIAO; Lin CHENG; Ying CHEN; Huizhe GUAN

doi:10.7500/AEPS20210629014

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Transient Stability Assessment of Power System Based on Spatio-Temporal Graph Convolutional Network

Basic Research | 更新时间：2025-06-16

- Transient Stability Assessment of Power System Based on Spatio-Temporal Graph Convolutional Network
- Automation of Electric Power Systems Vol. 46, Issue 11, Pages: 11-18(2022)
- 作者机构：
  
  清华大学电机工程与应用电子技术系，北京市 100084
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52037006);China Postdoctoral Science Foundation(2020TQ0160)
- DOI：10.7500/AEPS20210629014
  CLC：
- Received：29 June 2021，
  
  Revised：2021-12-29，
  
  Published Online：19 April 2022，
  
  Published：10 June 2022
- 稿件说明：
移动端阅览
庄颖睿,肖谭南,程林等.基于时空图卷积网络的电力系统暂态稳定评估[J].电力系统自动化,2022,46(11):11-18.

ZHUANG Yingrui,XIAO Tannan,CHENG Lin,et al.Transient Stability Assessment of Power System Based on Spatio-Temporal Graph Convolutional Network[J].Automation of Electric Power Systems,2022,46(11):11-18.
庄颖睿,肖谭南,程林等.基于时空图卷积网络的电力系统暂态稳定评估[J].电力系统自动化,2022,46(11):11-18. DOI： 10.7500/AEPS20210629014.

ZHUANG Yingrui,XIAO Tannan,CHENG Lin,et al.Transient Stability Assessment of Power System Based on Spatio-Temporal Graph Convolutional Network[J].Automation of Electric Power Systems,2022,46(11):11-18. DOI： 10.7500/AEPS20210629014.

摘要

快速准确的电力系统暂态稳定分析对电力系统安全稳定运行有着重要意义。现代电力系统设备元件日趋复杂多样导致系统非线性日益增强，作为电力系统暂态稳定分析传统方法的时域仿真法过于耗时。针对此问题，提出了一种基于时空图卷积网络模型的暂态稳定分析方法，将短时仿真与神经网络预测相结合，减少暂态稳定分析所需时间，可用于多种仿真分析场景。该方法将暂态稳定分析建模为样本空间映射问题，利用数据驱动方法训练神经网络模型，建立从暂态过程电网空间结构与时序潮流数据到暂态稳定的映射。模型通过同时提取暂态过程故障前、故障中、故障后的电网空间结构特征和时序潮流特征来实现对系统暂态稳定的快速准确判断。与传统暂态稳定分析方法相比，所提出的方法仅需进行短时间仿真分析，提高了分析效率。与其他机器学习模型相比，时空图卷积网络模型同时挖掘电力系统暂态过程的空间特征和时间特征，引入了更多与稳定性相关的先验知识，具有更优的特征挖掘能力和分析性能。基于新英格兰39节点系统的测试结果验证了所提方法的可行性、有效性和优越性。

Abstract

Fast and accurate transient stability analysis of the power system is of great significance to the safe and stable operation of the power system. With the increasing complexity and diversity of equipment components in the modern power system

the nonlinear characteristics of the system are increasingly strengthened. As a traditional transient stability analysis method of the power system

the time-domain simulation is too time-consuming. To solve this problem

a transient stability analysis method based on the spatio-temporal graph convolutional network (STGCN) is proposed

which combines short-term simulation and neural network prediction to reduce the time required for transient stability analysis and can be applied in a variety of simulation analysis scenarios. By modeling the transient stability analysis as a sample space mapping problem with the proposed method

a data-driven method is adopted to train the neural network model and establish a mapping from the spatial structure and temporal power flow data of power grid in the transient process to the transient stability. The model simultaneously extracts the characteristics of the spatial structure and temporal power flow of power grid before

during

and after the failure in the transient process to realize the fast and accurate judgment of the transient stability of the system. Compared with the traditional transient stability analysis method

the proposed method only needs to complete a short-time simulation analysis

which improves the analysis efficiency. Compared with other machine learning models

the STGCN model simultaneously discovers the spatial and temporal characteristics of the transient process of the power system

introduces more prior knowledge about stability

and has better characteristic extracting ability and analysis performance. Test results based on the New England 39-bus system verify the feasibility

effectiveness and superiority of the proposed method.

关键词

Keywords

references

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