Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems

Abstract The performance of non‐intrusive load monitoring (NILM) systems heavily depends on the uniqueness of the load signature extracted from the electrical appliances. Different load signatures have been proposed. Recently, in particular, v–i trajectory feature extraction is attracting more and m...

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Main Authors:	Ricardo Brito, Man‐Chung Wong, Hong Cai Zhang, Miguel Gomes Da Costa Junior, Chi‐Seng Lam, Chi‐Kong Wong
Format:	Article
Language:	English
Published:	Wiley 2021-02-01
Series:	IET Smart Grid
Online Access:	https://doi.org/10.1049/stg2.12008

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spelling	doaj-795f09cdae804db8a0584c16ebf5190d2021-04-20T13:45:31ZengWileyIET Smart Grid2515-29472021-02-015112113310.1049/stg2.12008Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systemsRicardo Brito0Man‐Chung Wong1Hong Cai Zhang2Miguel Gomes Da Costa Junior3Chi‐Seng Lam4Chi‐Kong Wong5State Key Laboratory of Internet of Things for Smart City University of Macau Taipa MacauState Key Laboratory of Internet of Things for Smart City University of Macau Taipa MacauState Key Laboratory of Internet of Things for Smart City University of Macau Taipa MacauDepartment of Computer and Information Science Faculty of Science and Technology, University of Macau Taipa MacauState Key Laboratory of Analog and Mixed Signal VLSI University of Macau Taipa MacauDepartment of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau Taipa MacauAbstract The performance of non‐intrusive load monitoring (NILM) systems heavily depends on the uniqueness of the load signature extracted from the electrical appliances. Different load signatures have been proposed. Recently, in particular, v–i trajectory feature extraction is attracting more and more attention due to its unique characteristics. Herein, instantaneous p–q load signature (IpqLS) feature extraction is first proposed and applied in NILM, which shows that conventional methods cannot distinguish load signatures under some situations. Applying IpqLS with several machine learning algorithms is not only extracting unique features compared to the overlapping problems of P–Q and v–i trajectory but also improving load classification accuracy. Simulations and experimental results verified the effectiveness of the proposed method.https://doi.org/10.1049/stg2.12008
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Ricardo Brito Man‐Chung Wong Hong Cai Zhang Miguel Gomes Da Costa Junior Chi‐Seng Lam Chi‐Kong Wong
spellingShingle	Ricardo Brito Man‐Chung Wong Hong Cai Zhang Miguel Gomes Da Costa Junior Chi‐Seng Lam Chi‐Kong Wong Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems IET Smart Grid
author_facet	Ricardo Brito Man‐Chung Wong Hong Cai Zhang Miguel Gomes Da Costa Junior Chi‐Seng Lam Chi‐Kong Wong
author_sort	Ricardo Brito
title	Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems
title_short	Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems
title_full	Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems
title_fullStr	Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems
title_full_unstemmed	Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems
title_sort	instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems
publisher	Wiley
series	IET Smart Grid
issn	2515-2947
publishDate	2021-02-01
description	Abstract The performance of non‐intrusive load monitoring (NILM) systems heavily depends on the uniqueness of the load signature extracted from the electrical appliances. Different load signatures have been proposed. Recently, in particular, v–i trajectory feature extraction is attracting more and more attention due to its unique characteristics. Herein, instantaneous p–q load signature (IpqLS) feature extraction is first proposed and applied in NILM, which shows that conventional methods cannot distinguish load signatures under some situations. Applying IpqLS with several machine learning algorithms is not only extracting unique features compared to the overlapping problems of P–Q and v–i trajectory but also improving load classification accuracy. Simulations and experimental results verified the effectiveness of the proposed method.
url	https://doi.org/10.1049/stg2.12008
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Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems

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