基于深度置信网络的电压暂降特征提取及源辨识方法

李丹奇; 梅飞; 张宸宇; 沙浩源; 郑建勇; 李陶然

doi:10.7500/AEPS20190306004

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基于深度置信网络的电压暂降特征提取及源辨识方法

研制与开发 | 更新时间：2025-06-16

- 基于深度置信网络的电压暂降特征提取及源辨识方法
- Deep Belief Network Based Method for Feature Extraction and Source Identification of Voltage Sag
- 电力系统自动化-方正平台 2020年44卷第4期页码：150-158
- 作者机构：
  
  1. 东南大学电气工程学院,江苏省南京市 210096
  2. 河海大学能源与电气学院,江苏省南京市 211100
  3. 国网江苏省电力有限公司电力科学研究院,江苏省南京市 211103
- 作者简介：
  
  李丹奇(1996—),女,通信作者,硕士研究生,主要研究方向:电力系统及其自动化。E-mail：ldq19960313@163.com
  梅飞(1982—),男,博士,主要研究方向:电力电子技术。E-mail：mario82@163.com
  张宸宇(1989—),男,博士,工程师,主要研究方向:电能质量治理技术。E-mail：yu_z@sina.com
- 基金信息：
  
  江苏省重点研发计划资助项目(BE2017030)
- DOI：10.7500/AEPS20190306004
  中图分类号：
- 收稿日期：2019-03-06，
  
  修回日期：2019-05-15，
  
  网络出版日期：2019-07-16，
  
  纸质出版日期：2020-02-25
- 稿件说明：
移动端阅览
李丹奇,梅飞,张宸宇等.基于深度置信网络的电压暂降特征提取及源辨识方法[J].电力系统自动化,2020,44(04):150-158.

LI Danqi,MEI Fei,ZHANG Chenyu,et al.Deep Belief Network Based Method for Feature Extraction and Source Identification of Voltage Sag[J].Automation of Electric Power Systems,2020,44(04):150-158.
李丹奇,梅飞,张宸宇等.基于深度置信网络的电压暂降特征提取及源辨识方法[J].电力系统自动化,2020,44(04):150-158. DOI： 10.7500/AEPS20190306004.

LI Danqi,MEI Fei,ZHANG Chenyu,et al.Deep Belief Network Based Method for Feature Extraction and Source Identification of Voltage Sag[J].Automation of Electric Power Systems,2020,44(04):150-158. DOI： 10.7500/AEPS20190306004.

摘要

提出一种基于深度置信网络(DBN)的电压暂降特征提取与暂降源辨识方法

利用DBN的特征提取能力对实测波形数据进行特征自提取

解决了人工提取特征过度依赖专家经验

受未知特征影响较大不具备一般性的问题。采用多隐层结构网络学习特征最终实现暂降源辨识。该模型集特征提取器与分类器于一体

优化了模型结构框架

提高了暂降源辨识效率。对模型最优参数进行选择

建立适用于电压暂降实测数据类型的DBN模型

对电网实测暂降数据进行特征提取与暂降源辨识

通过对比验证了DBN方法在特征提取与暂降源识别上的优越性

适用于实际工程。

Abstract

A method for voltage sag feature extraction and sag source identification based on deep belief network (DBN) is proposed. The feature extraction ability of DBN is used to extract the feature data from the measured waveform data

which solves the problem that the artificial features rely too much on expert experiences and are short of generality. The multi-hidden layer structure network learning feature is used to realize the identification of the sag source. The model integrates feature extractor and classifier together to optimize the model structure and improve the efficiency of sag source identification. The optimal parameters of the model are selected

and DBN model for the measured voltage sag data is established. The feature extraction and sag source identification of the real-time measured sag data of power grid are carried out. The superiority of DBN method in feature extraction and sag source identification is verified

which is available for practical engineering.

关键词

Keywords

references

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