Event-based S-transform approach for nonintrusive load monitoring

Khairuddin Khalid; Azah Mohamed; Hussain Shareef; Maytham Sabeeh

doi:10.15199/48.2016.05.38

Event-based S-transform approach for nonintrusive load monitoring

Khairuddin Khalid, Azah Mohamed, Hussain Shareef, Maytham Sabeeh

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy.

Original language	English
Pages (from-to)	194-198
Number of pages	5
Journal	Przeglad Elektrotechniczny
Volume	92
Issue number	5
DOIs	https://doi.org/10.15199/48.2016.05.38
Publication status	Published - Apr 1 2016
Externally published	Yes

Keywords

Artificial neural network
Feature extraction
Load recognition
S-transform

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.15199/48.2016.05.38

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abstract = "In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy.",

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AU - Khalid, Khairuddin

AU - Mohamed, Azah

AU - Shareef, Hussain

AU - Sabeeh, Maytham

PY - 2016/4/1

Y1 - 2016/4/1

N2 - In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy.

AB - In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy.

KW - Artificial neural network

KW - Feature extraction

KW - Load recognition

KW - S-transform

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Event-based S-transform approach for nonintrusive load monitoring

Abstract

Keywords

ASJC Scopus subject areas

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