Non-intrusive load identification based on CF-MF-SE joint feature

Aiming at the problems of the current non-intrusive load identification,such as too long model training time and low identification accuracy of electrical appliances with similar load characteristics,a non-intrusive load identification method based on CF-MF-SE joint feature was proposed.Based on the...

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Autores principales:	Guoqing AN, Yufei LIANG, Ziyao JIANG, Zheng LI, Qi AN, He CHEN, Qiang WANG, Jiacheng BAI
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	electrical measuring technology and its instrumentation; non-intrusive load identification; spectral entropy; support vector machine; particle swarm optimization Technology T
Acceso en línea:	https://doaj.org/article/1242fae63b104b89b618c424c6e7f7fd
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id	oai:doaj.org-article:1242fae63b104b89b618c424c6e7f7fd
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spelling	oai:doaj.org-article:1242fae63b104b89b618c424c6e7f7fd2021-11-23T07:09:07ZNon-intrusive load identification based on CF-MF-SE joint feature1008-154210.7535/hbkd.2021yx05004https://doaj.org/article/1242fae63b104b89b618c424c6e7f7fd2021-10-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202105004&flag=1&journal_https://doaj.org/toc/1008-1542Aiming at the problems of the current non-intrusive load identification,such as too long model training time and low identification accuracy of electrical appliances with similar load characteristics,a non-intrusive load identification method based on CF-MF-SE joint feature was proposed.Based on the steady-state current signal,the peak factor was extracted to represent the distortion degree of the waveform,the margin factor was extracted to represent the stability degree of the signal,the spectral entropy was extracted to represent the complexity degree of the spectrum structure,and PSO-SVM was combined to realize load identification.Experimental results show that this method can solve the problem that the electrical current waveform is too similar to identify successfully,reduce the training time,and improve the recognition accuracy and efficiency.This method introduces the vibration signal characteristics as load characteristics into the field of load identification,which provides a new idea for feature selection of non-invasive load identification technology.As a key feature sensitive to load,spectral entropy can significantly improve the identification rate when combined with other features,which provides reference for the flexible selection of load characteristics in practical application.[HQ]Guoqing ANYufei LIANGZiyao JIANGZheng LIQi ANHe CHENZheng LIQiang WANGJiacheng BAIHebei University of Science and Technologyarticleelectrical measuring technology and its instrumentation; non-intrusive load identification; spectral entropy; support vector machine; particle swarm optimizationTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 5, Pp 462-469 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	electrical measuring technology and its instrumentation; non-intrusive load identification; spectral entropy; support vector machine; particle swarm optimization Technology T
spellingShingle	electrical measuring technology and its instrumentation; non-intrusive load identification; spectral entropy; support vector machine; particle swarm optimization Technology T Guoqing AN Yufei LIANG Ziyao JIANG Zheng LI Qi AN He CHEN Zheng LI Qiang WANG Jiacheng BAI Non-intrusive load identification based on CF-MF-SE joint feature
description	Aiming at the problems of the current non-intrusive load identification,such as too long model training time and low identification accuracy of electrical appliances with similar load characteristics,a non-intrusive load identification method based on CF-MF-SE joint feature was proposed.Based on the steady-state current signal,the peak factor was extracted to represent the distortion degree of the waveform,the margin factor was extracted to represent the stability degree of the signal,the spectral entropy was extracted to represent the complexity degree of the spectrum structure,and PSO-SVM was combined to realize load identification.Experimental results show that this method can solve the problem that the electrical current waveform is too similar to identify successfully,reduce the training time,and improve the recognition accuracy and efficiency.This method introduces the vibration signal characteristics as load characteristics into the field of load identification,which provides a new idea for feature selection of non-invasive load identification technology.As a key feature sensitive to load,spectral entropy can significantly improve the identification rate when combined with other features,which provides reference for the flexible selection of load characteristics in practical application.[HQ]
format	article
author	Guoqing AN Yufei LIANG Ziyao JIANG Zheng LI Qi AN He CHEN Zheng LI Qiang WANG Jiacheng BAI
author_facet	Guoqing AN Yufei LIANG Ziyao JIANG Zheng LI Qi AN He CHEN Zheng LI Qiang WANG Jiacheng BAI
author_sort	Guoqing AN
title	Non-intrusive load identification based on CF-MF-SE joint feature
title_short	Non-intrusive load identification based on CF-MF-SE joint feature
title_full	Non-intrusive load identification based on CF-MF-SE joint feature
title_fullStr	Non-intrusive load identification based on CF-MF-SE joint feature
title_full_unstemmed	Non-intrusive load identification based on CF-MF-SE joint feature
title_sort	non-intrusive load identification based on cf-mf-se joint feature
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/1242fae63b104b89b618c424c6e7f7fd
work_keys_str_mv	AT guoqingan nonintrusiveloadidentificationbasedoncfmfsejointfeature AT yufeiliang nonintrusiveloadidentificationbasedoncfmfsejointfeature AT ziyaojiang nonintrusiveloadidentificationbasedoncfmfsejointfeature AT zhengli nonintrusiveloadidentificationbasedoncfmfsejointfeature AT qian nonintrusiveloadidentificationbasedoncfmfsejointfeature AT hechen nonintrusiveloadidentificationbasedoncfmfsejointfeature AT zhengli nonintrusiveloadidentificationbasedoncfmfsejointfeature AT qiangwang nonintrusiveloadidentificationbasedoncfmfsejointfeature AT jiachengbai nonintrusiveloadidentificationbasedoncfmfsejointfeature
_version_	1718416808550072320

Non-intrusive load identification based on CF-MF-SE joint feature

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