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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
作者:
作者单位:

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

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。

关键词:

基金项目:

江苏省重点研发计划资助项目(BE2017030)。

通信作者:

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


Deep Belief Network Based Method for Feature Extraction and Source Identification of Voltage Sag
Author:
Affiliation:

1.School of Electrical Engineering, Southeast University, Nanjing 210096, China;2.College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;3.Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China

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.

Keywords:

Foundation:
This work is supported by Key R&D Program of Jiangsu Province (No. BE2017030).
引用本文
[1]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(4):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(4):150-158. DOI:10.7500/AEPS20190306004.
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  • 收稿日期:2019-03-06
  • 最后修改日期:2019-05-15
  • 录用日期:2019-05-27
  • 在线发布日期: 2020-02-25
  • 出版日期:
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