基于特征融合与深度学习的非侵入式负荷辨识算法

王守相; 郭陆阳; 陈海文; 邓欣宇

doi:10.7500/AEPS20190625010

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基于特征融合与深度学习的非侵入式负荷辨识算法

学术研究 | 更新时间：2025-06-16

- 基于特征融合与深度学习的非侵入式负荷辨识算法
- Non-intrusive Load Identification Algorithm Based on Feature Fusion and Deep Learning
- 电力系统自动化-方正平台 2020年44卷第9期页码：103-110
- 作者机构：
  
  智能电网教育部重点实验室（天津大学），天津市 300072
- 作者简介：
  
  王守相(1973—)，男，博士，教授，博士生导师，主要研究方向：分布式发电与智能配电网。E-mail：sxwang@tju.edu.cn
  郭陆阳(1995—)，男，硕士研究生，主要研究方向：非侵入式负荷监测。E-mail：guoluyang@tju.edu.cn
  陈海文(1994—)，男，通信作者，博士研究生，主要研究方向：智能用电等。E-mail：chenhaiwen@tju.edu.cn
- 基金信息：
- DOI：10.7500/AEPS20190625010
  中图分类号：
- 收稿日期：2019-06-25，
  
  修回日期：2019-08-05，
  
  网络出版日期：2019-10-21，
  
  纸质出版日期：2020-05-10
- 稿件说明：
移动端阅览
王守相,郭陆阳,陈海文等.基于特征融合与深度学习的非侵入式负荷辨识算法[J].电力系统自动化,2020,44(9):103-110.

WANG Shouxiang,GUO Luyang,CHEN Haiwen,et al.Non-intrusive Load Identification Algorithm Based on Feature Fusion and Deep Learning[J].Automation of Electric Power Systems,2020,44(9):103-110.
王守相,郭陆阳,陈海文等.基于特征融合与深度学习的非侵入式负荷辨识算法[J].电力系统自动化,2020,44(9):103-110. DOI： 10.7500/AEPS20190625010.

WANG Shouxiang,GUO Luyang,CHEN Haiwen,et al.Non-intrusive Load Identification Algorithm Based on Feature Fusion and Deep Learning[J].Automation of Electric Power Systems,2020,44(9):103-110. DOI： 10.7500/AEPS20190625010.

摘要

针对使用单一设备特征进行负荷辨识存在的局限性，提出了一种基于特征融合与深度学习的非侵入式负荷辨识算法。通过分析设备的高频采样数据提取了

轨迹图像特征与功率数值特征。利用人工神经网络的高级特征提取能力，实现了

轨迹图像特征与功率数值特征的融合。最后以复合特征作为设备新的特征训练反向传播(BP)神经网络进行非侵入式负荷辨识。使用PLAID数据集对算法辨识效果进行了验证，并对比了不同分类算法对特征融合的有效性与负荷辨识能力。结果表明，该算法利用不同特征之间的互补性，克服了使用

轨迹特征无法反映设备功率大小的缺点，从而提高了

轨迹特征的负荷辨识能力，并且在嵌入式设备中的运算速度为毫秒级。

Abstract

Aiming at the limitation of using single equipment features for load identification

a non-intrusive load identification algorithm based on feature fusion and deep learning is proposed. Firstly

trajectory image features and power numerical features are extracted by analyzing the high-frequency sampling data of the equipment. Then the fusion of

trajectory image features and power numerical features is realized by using the advanced feature extraction ability of artificial neural network (ANN). Finally

the back propagation (BP) neural network is trained to identify equipment by using fusion feature as the new feature of the equipment. The PLAID data set is used to verify the identification performance of the algorithm

and the performances of different classification algorithms are compared for feature fusion and load identification ability. The results show that the proposed algorithm makes use of the complementarity of different features

overcomes the disadvantage that

trajectory features cannot reflect the power of the equipment

and improves the load identification ability of

trajectory features. In embedded devices

the computing speed of the proposed algorithm can reach the millisecond level.

关键词

Keywords

references

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