Non-intrusive Load Identification by Combined Support Vector Machine Based on Structured Characteristic Spectrum

Xin WU; Meng YAN; Yifan GUO; Kaiyan HUANG; Dian JIAO

doi:10.7500/AEPS20210924004

您当前的位置：

首页 >

文章列表页 >

Non-intrusive Load Identification by Combined Support Vector Machine Based on Structured Characteristic Spectrum

Basic Research | 更新时间：2025-06-16

- Non-intrusive Load Identification by Combined Support Vector Machine Based on Structured Characteristic Spectrum
- Automation of Electric Power Systems Vol. 46, Issue 12, Pages: 210-219(2022)
- 作者机构：
  
  1.华北电力大学电气与电子工程学院，北京市 102206
  2.北京电力经济技术研究院有限公司，北京市 100008
- 作者简介：
- 基金信息：
  
  the Fundamental Research Funds for the Central Universities(2020MS002)
- DOI：10.7500/AEPS20210924004
  CLC：
- Received：24 September 2021，
  
  Revised：2022-04-18，
  
  Published Online：25 April 2022，
  
  Published：25 June 2022
- 稿件说明：
移动端阅览
武昕,严萌,郭一凡等.基于结构化特征图谱的组合支持向量机非侵入式负荷辨识[J].电力系统自动化,2022,46(12):210-219.

WU Xin,YAN Meng,GUO Yifan,et al.Non-intrusive Load Identification by Combined Support Vector Machine Based on Structured Characteristic Spectrum[J].Automation of Electric Power Systems,2022,46(12):210-219.
武昕,严萌,郭一凡等.基于结构化特征图谱的组合支持向量机非侵入式负荷辨识[J].电力系统自动化,2022,46(12):210-219. DOI： 10.7500/AEPS20210924004.

WU Xin,YAN Meng,GUO Yifan,et al.Non-intrusive Load Identification by Combined Support Vector Machine Based on Structured Characteristic Spectrum[J].Automation of Electric Power Systems,2022,46(12):210-219. DOI： 10.7500/AEPS20210924004.

摘要

非侵入式负荷监测是获取负荷数据、实现负荷感知的有效途径。为了使非侵入式负荷监测过程具有通用性和实用性，在不干扰用户情况下自动执行流程并达到高辨识精度，研究了一种结构化特征图谱下的组合支持向量机辨识方法。构建典型负荷的特征图谱将变化无序的波形数据转化为结构化特征数据，使其具有通用性与可分性。在结构化特征图谱基础上，研究构建典型负荷的支持向量机分类器模型，在基分类器基础上形成每类负荷的组合支持向量机分类器，利用“集弱成强”思想保证每类组合分类器具有高分类准确率，从而实现准确的负荷辨识。在构建形成通用的图谱与分类器模型基础上，即可通过事件波形提取、波形数据结构化及分类器判决的处理流程实现实时的非侵入式负荷辨识。通过实际采集的负荷数据进行验证，构建了典型负荷的特征图谱，基于组合支持向量机模型对多户的采集数据进行分类判决，对不同用户的负荷数据均达到了高准确率辨识，验证了该方法具有较好的通用性与有效性。

Abstract

Non-intrusive load monitoring is an effective way to obtain load data and realize load perception. A combined support vector machine identification method for making the process of the non-intrusive load monitoring universal and practical based on the structured characteristic spectrum

is studied to automatically execute the process without disturbing users and achieve high identification accuracy. By constructing the characteristic spectrum of typical loads

the disordered waveform data are transformed into structured signature data

which makes the signature graph universal and distinguishable. Based on the structured characteristic spectrum

the support vector machine classifier model of typical loads is established

and the combined support vector machine classifier of each type of load is formed based on the base classifier model. The idea of “gathering weak into strong” is used to ensure that each combined classifier has high classification accuracy

so as to realize the accurate load identification. Based on the universal graph and the classifier model

the real-time non-intrusive load identification can be realized through the processing flow of event waveform extraction

waveform data structuration and classifier decision. By the acquired actual load data for verification

the characteristic spectrum of the typical load is developed

the collected data of multiple households based on the combined support vector machine model are classified

and the high accuracy identification for the load data of different users is achieved

which verifies that the proposed method has good universality and effectiveness.

关键词

Keywords

references

数字电网推动构建以新能源为主体的新型电力系统白皮书［EB/OL］. http：//www.chinasmartgrid.com.cn/news/20210425/638462.shtml http://www.chinasmartgrid.com.cn/news/20210425/638462.shtml .

Digital power grid promotes the construction of a new power system with new energy as the main body ［EB/OL］. http： //www.chinasmartgrid.com.cn/news/20210425/638462.shtml http://www.chinasmartgrid.com.cn/news/20210425/638462.shtml .

王彩霞，时智勇，梁志峰，等 . 新能源为主体电力系统的需求侧资源利用关键技术及展望［J］. 电力系统自动化， 2021 ， 45 （ 16 ）： 37 - 48 .

WANG Caixia ， SHI Zhiyong ， LIANG Zhifeng ， et al . Key technologies and prospects of demand-side resource utilization for power systems dominated by renewable energy ［J］. Automation of Electric Power Systems ， 2021 ， 45 （ 16 ）： 37 - 48 .

YILMAZ S ， CHAMBERS J ， PATEL M K . Comparison of clustering approaches for domestic electricity load profile characterisation—implications for demand side management ［J］. Energy ， 2019 ， 180 ： 665 - 677 .

武昕，焦点，高宇辰 . 基于非侵入式用电数据分解的自适应特征库构建与负荷辨识［J］. 电力系统自动化， 2020 ， 44 （ 4 ）： 101 - 109 .

WU Xin ， JIAO Dian ， GAO Yuchen . Construction of adaptive feature library and load identification based on decomposition of non-intrusive power consumption data ［J］. Automation of Electric Power Systems ， 2020 ， 44 （ 4 ）： 101 - 109 .

FAUSTINE A ， PEREIRA L ， KLEMENJAK C . Adaptive weighted recurrence graphs for appliance recognition in non-intrusive load monitoring ［J］. IEEE Transactions on Smart Grid ， 2021 ， 12 （ 1 ）： 398 - 406 .

肖潇，栾文鹏，刘博，等 . 基于电器粗糙归类的无监督NILM结果自主标注［J/OL］. 中国电机工程学报［ 2021-12-27 ］. https：//kns.cnki.net/kns8/defaultresult/index https://kns.cnki.net/kns8/defaultresult/index .

XIAO Xiao ， LUAN Wenpeng ， LIU Bo ， et al . Autonomous labeling of unsupervised NILM results based on rough classification of appliances ［J/OL］. Proceedings of the CSEE ［ 2021-12-27 ］. https：//kns.cnki.net/kns8/defaultresult/index https://kns.cnki.net/kns8/defaultresult/index .

WELIKALA S ， THELASINGHA N ， AKRAM M ， et al . Implementation of a robust real-time non-intrusive load monitoring solution ［J］. Applied Energy ， 2019 ， 238 ： 1519 - 1529 .

WANG S X ， CHEN H W ， GUO L Y ， et al . Non-intrusive load identification based on the improved voltage-current trajectory with discrete color encoding background and deep-forest classifier ［J］. Energy and Buildings ， 2021 ， 244 ： 111043 .

解洋，梅飞，郑建勇，等 . 基于 V - I 轨迹颜色编码的非侵入式负荷识别方法［J］. 电力系统自动化， 2022 ， 46 （ 4 ）： 93 - 102 .

XIE Yang ， MEI Fei ， ZHENG Jianyong ， et al . Non-intrusive load monitoring method based on V - I trajectory color coding ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 4 ）： 93 - 102 .

LIU H ， YU C M ， WU H P ， et al . An improved non-intrusive load disaggregation algorithm and its application ［J］. Sustainable Cities and Society ， 2020 ， 53 ： 101918 .

杨立余，陈昊，黎明，等 . 非侵入式电力负荷多目标分解框架［J］. 电力系统保护与控制， 2020 ， 48 （ 6 ）： 100 - 107 .

YANG Liyu ， CHEN Hao ， LI Ming ， et al . A framework for non-intrusive load monitoring using multi-objective evolutionary algorithms ［J］. Power System Protection and Control ， 2020 ， 48 （ 6 ）： 100 - 107 .

LI L ， YANG L Y ， CHEN H ， et al . Multi-objective evolutionary algorithms applied to non-intrusive load monitoring ［J］. Electric Power Systems Research ， 2019 ， 177 ： 105961 .

FAN W ， LIU Q ， AHMADPOUR A ， et al . Multi-objective non-intrusive load disaggregation based on appliances characteristics in smart homes ［J］. Energy Reports ， 2021 ， 7 ： 4445 - 4459 .

YUAN J ， WANG H L ， WU P ， et al . A novel current signal feature and its application in noninvasive load monitoring ［J］. IEEE Transactions on Instrumentation and Measurement ， 2021 ， 70 ： 1 - 10 .

田正其，徐晴，李如意，等 . 一种融合时间特征的非侵入式负荷辨识决策方法［J/OL］. 电测与仪表［ 2021-12-29 ］. https：// kns-cnki-net.webvpn.ncepu.edu.cn/kcms/detail/23.1202.TH.20200803.1424.016.html https://kns-cnki-net.webvpn.ncepu.edu.cn/kcms/detail/23.1202.TH.20200803.1424.016.html .

TIAN Zhengqi ， XU Qing ， LI Ruyi ， et al . Non-intrusive load identification decision method based on time signatures ［J/OL］. Electrical Measurement & Instrumentation ［ 2021-12-29 ］. https：//kns-cnki-net.webvpn.ncepu.edu.cn/kcms/detail/23.1202.TH.20200803.1424.016.html https://kns-cnki-net.webvpn.ncepu.edu.cn/kcms/detail/23.1202.TH.20200803.1424.016.html .

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 .

WELIKALA S ， DINESH C ， EKANAYAKE M P B ， et al . Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting ［J］. IEEE Transactions on Smart Grid ， 2017 ， 10 （ 1 ）： 448 - 461 .

韩笑，邓春宇，张玉天，等 . 基于高频数据 V - I 特性的延时反馈负荷在线快速辨识算法［J］. 电力系统自动化， 2019 ， 43 （ 9 ）： 108 - 116 .

HAN Xiao ， DENG Chunyu ， ZHANG Yutian ， et al . Fast online identification algorithm for delayed feedback load based on V - I characteristics of high frequency data ［J］. Automation of Electric Power Systems ， 2019 ， 43 （ 9 ）： 108 - 116 .

尹立亚 . 非侵入式负荷特征提取与识别算法的研究［D］. 北京：华北电力大学， 2021 .

YIN Liya . Research on non-invasive load feature extraction and identification algorithm ［D］. Beijing ： North China Electric Power University ， 2021 .

郭陆阳 . 基于深度学习的非侵入式负荷监测技术研究［D］. 天津：天津大学， 2020 .

GUO Luyang . Research on non-invasive load monitoring technology based on deep learning ［D］. Tianjin ： Tianjin University ， 2020 .

刘丽 . 基于多特征融合的居民用电行为识别技术研究［D］. 合肥：安徽大学， 2019 .

LIU Li . Research on residential electricity behavior recognition technology based on multi-feature fusion ［D］. Hefei ： Anhui University ， 2019 .

D’INCECCO M ， SQUARTINI S ， ZHONG M J . Transfer learning for non-intrusive load monitoring ［J］. IEEE Transactions on Smart Grid ， 2020 ， 11 （ 2 ）： 1419 - 1429 .

ARJMAND A ， SAMIZADEH R ， DEHGHANI SARYAZDI M . Meta-learning in multivariate load demand forecasting with exogenous meta-features ［J］. Energy Efficiency ， 2020 ， 13 （ 5 ）： 871 - 887 .

祁兵，董超，武昕，等 . 基于DTW算法与稳态电流波形的非侵入式负荷辨识方法［J］. 电力系统自动化， 2018 ， 42 （ 3 ）： 70 - 76 .

QI Bing ， DONG Chao ， WU Xin ， et al . Non-intrusive load identification method based on DTW algorithm and steady-state current waveform ［J］. Automation of Electric Power Systems ， 2018 ， 42 （ 3 ）： 70 - 76 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Non-intrusive Perception and Identification of Industrial Power Load Based on Analysis of Event on User Edge

一种适用于非侵入式负荷监测的暂态事件检测算法

Load Identification and Its Interpretable Interactive Enhancement Method Based on Multi-scale Feature Fusion

Non-intrusive Real-time Monitoring System for Electric Bicycle Charging Based on Wavelet Detection and Feature Graph Decision

Related Author

Kaiyan HUANG

Yifan GUO

Meng YAN

Xin WU

Yulong YAN

Qinghua WANG

Jinying YU

Lin PENG

Related Institution

School of Electrical and Electronic Engineering of North China Electric Power University

School of Environmental Science and Engineering, North China Electric Power University

School of Control and Computer Engineering, North China Electric Power University

Tianjin University

Hebi Colledge of Vocation and Technology

⁰