基于双向长短期记忆网络的电力系统暂态稳定评估

孙黎霞; 白景涛; 周照宇; 赵晨昀

doi:10.7500/AEPS20191225003

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基于双向长短期记忆网络的电力系统暂态稳定评估

学术研究 | 更新时间：2025-06-16

- 基于双向长短期记忆网络的电力系统暂态稳定评估
- Transient Stability Assessment of Power System Based on Bi-directional Long-short-term Memory Network
- 电力系统自动化-方正平台 2020年44卷第13期页码：64-72
- 作者机构：
  
  河海大学能源与电气学院，江苏省南京市 211100
- 作者简介：
  
  孙黎霞（1978—），女，博士，副教授，硕士生导师，主要研究方向：电力电子在电力系统中的应用、同步发电机及其励磁系统的建模与参数辨识、非线性系统的控制等。E-mail：Lixiasun@hhu.edu.cn
  白景涛（1994—），男，通信作者，硕士研究生,主要研究方向：人工智能在电力系统中的应用、非线性系统控制等。E-mail：1830363811@qq.com
  周照宇(1996—)，男，硕士研究生，主要研究方向：风力发电系统建模。E-mail：69527274@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(51707056)
- DOI：10.7500/AEPS20191225003
  中图分类号：
- 收稿日期：2019-12-25，
  
  修回日期：2020-03-12，
  
  网络出版日期：2020-05-13，
  
  纸质出版日期：2020-07-10
- 稿件说明：
移动端阅览
孙黎霞,白景涛,周照宇等.基于双向长短期记忆网络的电力系统暂态稳定评估[J].电力系统自动化,2020,44(13):64-72.

SUN Lixia,BAI Jingtao,ZHOU Zhaoyu,et al.Transient Stability Assessment of Power System Based on Bi-directional Long-short-term Memory Network[J].Automation of Electric Power Systems,2020,44(13):64-72.
孙黎霞,白景涛,周照宇等.基于双向长短期记忆网络的电力系统暂态稳定评估[J].电力系统自动化,2020,44(13):64-72. DOI： 10.7500/AEPS20191225003.

SUN Lixia,BAI Jingtao,ZHOU Zhaoyu,et al.Transient Stability Assessment of Power System Based on Bi-directional Long-short-term Memory Network[J].Automation of Electric Power Systems,2020,44(13):64-72. DOI： 10.7500/AEPS20191225003.

摘要

为进一步提升电力系统暂态稳定评估的准确率，依据电力系统暂态过程数据的时序特性，建立了一种基于双向长短期记忆(Bi-LSTM)网络的暂态稳定评估模型。该方法通过Bi-LSTM网络建立底层量测数据与电力系统暂态稳定类别之间的非线性映射关系，采用准确率、F1指标和FPR指标综合评估Bi-LSTM网络模型性能的优劣，在此基础上，采用

分布随机近邻嵌入(

-SNE)降维方法和

最近邻(KNN)分类器进一步提升暂态稳定评估的准确率。新英格兰10机39节点系统算例表明：所提模型比传统的机器学习模型和部分深度学习模型拥有更好的评估性能。通过可视化方法和网络预测分数对评估模型进行分析，结果表明Bi-LSTM网络模型具有较强的电力系统暂态过程特征提取能力，适用于电力系统暂态稳定性的评估。进一步研究了底层输入数据的归一化模式和方法对暂态评估模型的影响，结果表明z-score归一化方法要优于min-max归一化方法，采用总维数归一化模式的模型评估性能更好。

Abstract

In order to further improve the accuracy of transient stability assessment (TSA)

a TSA model based on the bi-directional long-short-term memory (Bi-LSTM) network is established according to the sequential characteristics of data in the power system transient process. This method uses the Bi-LSTM network to establish a non-linear mapping relationship between the basic measurement data and the transient stability category of power system. The performance of Bi-LSTM network model is evaluated by accuracy

F1-measure (F1) index and false positive rate (FPR). On this basis

the

-distribution stochastic neighbor embedding (

-SNE) dimension-reduction method and the

-nearest neighbor (KNN) classifier are used to further improve the accuracy of TSA. The example based on the New England 10-generator 39-bus system show that the proposed method has better performance than conventional machine learning models and some deep learning models. The assessment model is analyzed by visualization methods and network prediction scores. The results show that the Bi-LSTM network has a strong ability to extract the characteristics of power system transient process

which is suitable for the TSA of power system. Further

the influence of the normalization mode and method of basic input data on the TSA model is studied. The results show that the z-score normalization method is better than min-max normalization method

and the assessment performance of the model using the total dimension normalization mode is better.

关键词

Keywords

references

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