基于SPWVD图像和深度迁移学习的强迫振荡源定位方法

冯双; 陈佳宁; 汤奕; 史豪

doi:10.7500/AEPS20191225001

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基于SPWVD图像和深度迁移学习的强迫振荡源定位方法

学术研究 | 更新时间：2025-06-16

- 基于SPWVD图像和深度迁移学习的强迫振荡源定位方法
- Location Method of Forced Oscillation Source Based on SPWVD Image and Deep Transfer Learning
- 电力系统自动化-方正平台 2020年44卷第17期页码：78-87
- 作者机构：
  
  1.东南大学电气工程学院，江苏省南京市 210096
  2.国网扬州供电公司，江苏省扬州市 225000
- 作者简介：
  
  冯双(1990—)，女，通信作者，博士，讲师，主要研究方向：电力系统运行与控制、电力电子化电力系统稳定性分析与控制。E-mail：sfeng@seu.edu.cn
  陈佳宁(1995—)，男，硕士研究生，主要研究方向：电力系统稳定分析。E-mail：plutoteris@126.com
  汤奕(1977—)，男，博士，教授，博士生导师，主要研究方向：电力系统稳定分析、电力信息物理系统。E-mail：tangyi@seu.edu.cn
- 基金信息：
  
  国家重点研发计划资助项目(2018YFB0904500)
- DOI：10.7500/AEPS20191225001
  中图分类号：
- 收稿日期：2019-12-25，
  
  修回日期：2020-04-24，
  
  网络出版日期：2020-06-02，
  
  纸质出版日期：2020-09-10
- 稿件说明：
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冯双,陈佳宁,汤奕等.基于SPWVD图像和深度迁移学习的强迫振荡源定位方法[J].电力系统自动化,2020,44(17):78-87.

FENG Shuang,CHEN Jianing,TANG Yi,et al.Location Method of Forced Oscillation Source Based on SPWVD Image and Deep Transfer Learning[J].Automation of Electric Power Systems,2020,44(17):78-87.
冯双,陈佳宁,汤奕等.基于SPWVD图像和深度迁移学习的强迫振荡源定位方法[J].电力系统自动化,2020,44(17):78-87. DOI： 10.7500/AEPS20191225001.

FENG Shuang,CHEN Jianing,TANG Yi,et al.Location Method of Forced Oscillation Source Based on SPWVD Image and Deep Transfer Learning[J].Automation of Electric Power Systems,2020,44(17):78-87. DOI： 10.7500/AEPS20191225001.

摘要

强迫振荡扰动源的准确定位是消除强迫振荡、恢复电力系统正常运行的关键。文中提出一种基于平滑伪Wigner-Ville分布（SPWVD）图像和深度迁移学习的强迫振荡扰动源定位方法。首先对强迫振荡信号采用SPWVD方法以图像形式表征全网强迫振荡特征信息，然后通过深度迁移学习将其他领域的图像识别知识迁移到电力系统领域，挖掘振荡图像与扰动源位置之间的联系，在保证训练准确度的同时，提升了训练效率。在WECC 179节点系统中的算例验证了该方法的有效性，并且相比于传统机器学习方法具有准确率高的优势。此外还考虑振荡数据中的噪声、录波起始时间以及数据长度验证了所提方法的准确性和抗噪性，并在由负荷引发的强迫振荡和系统拓扑发生变化的情况下，验证了方法的有效性。

Abstract

Accurately locating forced oscillation disturbance sources is the key to eliminate forced oscillation and restore normal operation of power system. A location method of forced oscillation disturbance source based on smoothed pseudo Wigner-Ville distribution (SPWVD) image and deep transfer learning is proposed. Firstly

for the forced oscillation signals

SPWVD is adopted to represent the forced oscillation characteristic information of the whole power system in the form of image. Then

through deep transfer learning

the image recognition knowledge from other fields is transferred into the power system to mine the relationship between SPWVD images and the location of disturbance sources

which guarantees the training accuracy and improves the training efficiency. Finally

the effectiveness of the proposed method is verified in WECC 179-bus system

and it has higher accuracy than traditional machine learning method. In addition

considering the noise

the start time of recording and data length in the oscillation data

the accuracy and noise resistance of the proposed method are verified under the condition of forced oscillation induced by load fluctuation and changing system topology.

关键词

Keywords

references

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