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

Krzysztof Dowalla, Piotr Bilski, Robert Łukaszewski, Augustyn Wójcik and Ryszard Kowalik
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Krzysztof Dowalla: Institute of Radioelectronics and Multimedia Technologies, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Piotr Bilski: Institute of Radioelectronics and Multimedia Technologies, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Robert Łukaszewski: Institute of Radioelectronics and Multimedia Technologies, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Augustyn Wójcik: Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
Ryszard Kowalik: Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland

Energies, 2022, vol. 15, issue 9, 1-20

Abstract: 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.

Keywords: NILM; smart grid; smart metering; load disaggregation; electrical appliances; non-intrusive load monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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