An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction

LI Shuaihu; ZHAO Xiang; JIANG Yunchen

doi:10.3969/j.issn.1008-0198.2022.05.004

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An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction

更新时间：2025-06-11

- An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction
- Vol. 42, Issue 5, Pages: 22-28(2022)
- 作者机构：
  
  1. 长沙理工大学电气与信息工程学院,湖南,长沙,410014
  2. 国网湖南省电力有限公司长沙供电分公司,湖南,长沙,410011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2022.05.004
  CLC： TM712.2
- Published：2022
- 稿件说明：
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李帅虎, 赵翔, 蒋昀宸. 基于粒子群优化算法支持向量回归预测法的大电网电压稳定在线评估方法[J]. 湖南电力, 2022, 42(5): 22-28.

LI Shuaihu, ZHAO Xiang, JIANG Yunchen. An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction[J]. 2022, 42(5): 22-28.
李帅虎, 赵翔, 蒋昀宸. 基于粒子群优化算法支持向量回归预测法的大电网电压稳定在线评估方法[J]. 湖南电力, 2022, 42(5): 22-28. DOI： 10.3969/j.issn.1008-0198.2022.05.004.

LI Shuaihu, ZHAO Xiang, JIANG Yunchen. An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction[J]. 2022, 42(5): 22-28. DOI： 10.3969/j.issn.1008-0198.2022.05.004.

摘要

提出基于粒子群优化算法支持向量回归预测法(particle swarm optimization support vector regression

PSO-SVR)的大电网电压稳定在线评估方法

将传统基于深度神经网络(deep neural networks

DNN)模型的电压稳定评估方法改进为PSO优化过的SVR模型

对阻抗模裕度指标进行预测。该方法利用了SVR模型具有学习能力强、泛化错误率低的优点

在小样本的情况下也可以很好地学习到样本中的特征。同时克服SVR模型对于参数调节和函数选择非常敏感的问题

利用PSO算法对SVR模型的超参数进行优化选择

可以让SVR模型更好地学习到电网运行数据和阻抗模裕度值之间的非线性关系。最后

该方法在IEEE 118节点系统进行验证

并与基于DNN模型的评估方法进行比较

验证了其精度水平高于基于DNN模型的方法。

Abstract

In this paper

an online voltage stability evaluation method based on particle swarm optimization support vector regression(PSO-SVR) for large power grid is proposed. The traditional voltage stability evaluation method based on deep neural networks(DNN) model is improved to the SVR model optimized by PSO to predict the impedance modulus margin index.This method takes advantage of SVR model′s advantages of strong learning ability and low generalization error rate

and it can also learn the features of samples well in the case of small samples.In order to overcome SVR model is very sensitive to parameter adjustment and function selection.The proposed method uses PSO algorithm to optimize the hyper-parameters of SVR model

which can make the SVR model better learn the nonlinear relationship between power grid operation data and impedance modulus margin value.Finally

the proposed method is validated in IEEE 118 node system

and the accuracy of the proposed method is higher than that of DNN model-based evaluation method.

关键词

Keywords

references

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