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

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.

Series arc fault detection and line selection based on Non-Intrusive Load Monitoring Method

“…1. The measuring system was adapted from [6]. Only Medium Frequency (MF) Unit with Advantech PCIE 1816 data acquisition card (DAQ) was used.…”

“…Signals 𝑢 𝑡𝑜𝑡𝑎𝑙 , 𝑖 𝑡𝑜𝑡𝑎𝑙 and 𝑖 𝑗 from each registered sequence were transformed, as described in [6] to arrays 𝑈 𝐼 and 𝐼 𝑎𝑟𝑐 , respectively. Each column in the array represents samples from the single 50 Hz period 𝑘 = 1, … , 𝐾 .…”

“…This paper proposes a novel method for arc detection and line selection method. It's based on the NILM method proposed in [6], where the analysis is performed in the time domain. Periodicity of the network voltage signal is exploited, allowing for dividing the signal into separate periods.…”

“…That allows for identifying the electrical network branch on which arc fault occurs, called line selection. The proposed method can enhance the functionality of the NILM system as in [6] analyzed signals are collected at a single point of the electrical network.…”

Optimized LightGBM Power Fingerprint Identification Based on Entropy Features

“…Earlier, the steady state characteristics of active power P and reactive power Q were often used for identification [ 6 ]. References [ 7 , 8 ] added features such as current waveform, harmonics, transient power waveform, and switching transient waveform for load identification based on the use of active and reactive power. In [ 9 ], the voltage and current signals were converted into two-dimensional images in combination with the V-I trajectory for load identification.…”