Electrical transient modeling for appliance characterization

Abstract

Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.

A Multi-Agent NILM Architecture for Event Detection and Load Classification

“…Changes in power or current are associated with signal analysis in time-domain, in which the event detection is based, for instance, on the apparent power to determine the instant of an event, as presented in the Half-Cycle Apparent Power (HCApP) method, proposed in [9]. A similar approach, using the envelope of electrical current and an adaptive threshold-based method, is presented in [10,11], defined as High-Accuracy NILM Detector (HAND). Furthermore, considering the need to detect signatures based on state change characteristics, in [12], an event detection is presented for multiple state loads, using the concept of powerlets [13], which were defined as groups of short sequences that characterize an appliance.…”

“…In [10], a modification of the Fourier Transform, incorporating an exponential damping, is proposed. The authors demonstrate that the identification of the damping observed during the transients improves the classification performance when combined with Fourier coefficients, achieving an accuracy of 90-99% for the COOLL dataset, even for unsupervised approaches.…”

“…Their findings are reported to the Event Detection Composition block, whose decision of whether a load event occurred or not is based on a combination of the individual contributions of the EDAs. In this work, the following EDAs were selected: (i) Discrete Wavelet Transform (DWT) [14,15]; (ii) Kalman Filter (KF) [8]; (iii) Half-Cycle Apparent Power (HCApP) [9]; (iv) High-Accuracy NILM Detector (HAND) [10,11]; (v) Vectorization (VEC). The criterion for choosing the EDAs was to have at least one method representing each group (see Section 2): (i) changes in power or current (HCApP, HAND, and VEC), (ii) high-pass filters (DWT), (iii) and prominent residuals from models (KF).…”

“…The HAND (High-Accuracy NILM Detector) approach, presented in [10,11], tracks the variation of the standard deviation of the current signal envelope using a moving window. The peak value of the current in each mains cycle is extracted; then, a cubic interpolation among all the peaks is performed.…”

“…As previously shown in [10,43], the transient currents of electrical equipment (also called inrush currents) can be used to classify loads. These currents are characterized by their high values right after the load starts consuming energy and decreasing values as the steady-state is reached.…”