A Distinctive Load Feature Extraction Based on Fryze's Time-domain Power Theory

Abstract

The performance of a non-intrusive load monitoring (NILM) system heavily depends on the uniqueness of the preferred load signature (LS) extracted from each appliance. Some electrical characteristics such as instantaneous current waveform (CW), instantaneous power waveform (IPW), current harmonic (CH) and voltage-current (V-I) trajectory have been proposed as appliance features in the literature. However, in some situations, these LSs cannot effectively distinguish different loads apart. In this paper, a time-domain based advanced power theory is used to decompose the load current into active and non-active orthogonal components. Then, two new LSs have been established based on the non-active component of the load current, namely the non-active current waveform (i f ) and the voltage-non-active current (V-I f ) characteristics curve. Simulation and experimental tests show that both of these features can distinguish different appliances. Hence, the proposed LSs can significantly enhance the existing NILM systems.

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

“…Nevertheless, several highly resistive loads may not be detected by this approach due to the low level of the current harmonics. The applications of V-I trajectory in NILM are discussed in [9,10]. In this scheme, the instantaneous voltage and current are respectively measured and normalized based on their peaks.…”

Smart Home Meter Profiler with Load Authentication, Shock Protection, Fault Proof and Restricted Demand Management

“…To detect the appliance ID using SMID-AO, we used a V-I trajectories curve and Fryze's Power Theory, explained further by Teshome et al, (2016) and implemented in MatLAB, as shown in Figure 5b, where the V-I trajectories are compared with a ready data set of appliance waveforms using the correlation coefficient of waveform method. Figures 5a and 5c show MatLAB Simulink simulation data inserted using an excel spreadsheet of voltage and iron appliance currents.…”

Load Disaggregation Using Microscopic Power Features and Pattern Recognition

“…Using power theory concepts, Teshome et al [44] proposed a NILM with components of active, reactive, apparent power, and nonactive currents. Nguyen et al [45] created a NILM method based on active, reactive and apparent power and used a decision tree (DT) to disaggregate five loads, achieving more than 98.8% accuracy.…”

“…To summarize, there are many other studies regarding NILM with different methodologies, feature extraction, different appliances in the validation and different load disaggregation algorithms. Nevertheless, in Teshome et al [44], the authors indicate the importance of modern power theories and the lack of these elegant circuit analyses to improve the NILM systems. One of these elegant power theories pointed out in Teshome et al [44] was the CPT.…”