4.5 Article

Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring

Journal

ENERGIES
Volume 15, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/en15093325

Keywords

NILM; smart grid; smart metering; load disaggregation; electrical appliances; non-intrusive load monitoring

Categories

Funding

  1. POB Energy ofWarsaw University of Technology within the Excellence Initiative: Research University (IDUB) program [1820/343/Z01/POB7/2021]

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The paper presents a novel non-intrusive method for identifying appliances by analyzing changes in the common supply current signal. The method achieves high accuracy by processing the signal in the time domain, preserving the information in the original signal. Experimental results demonstrate the effectiveness of the approach in real-world scenarios.
The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the common supply current signal. Analysis of the instantaneous changes in the aggregated current on the output of the analyzed circuit in the power network is exploited here. The signal is processed using the time alignment of the current and voltage signals samples represented in the array form. The scheme includes filtering, event detection and identification, which is performed by comparing parameters of the detected event against previously determined signatures of monitored appliances. The analysis is performed in the time domain; therefore (unlike other existing methods), the information contained in the original signal is not lost. The approach was tested in the laboratory designed specifically for this purpose. All tests have been conducted with up to 12 appliances operating at the same time in the single power supply circuit. The measurement setup was developed and used to record appliances' switching on/off events. During tests, 2300 events for devices were recorded. Collected data were processed to identify particular devices with the accuracy of 98.8% and macro-averaged F-score measure of 0.9874. High identification accuracy was achieved despite the high number of devices operating in the background.

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