Harmonic Loads Classification by Means of Currents’ Physical Components

Abstract

Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents’ physical components-based power theory decomposition. Using currents’ physical components ensures a constant number of features, which maintains the signal’s characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components.

Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation

“…where: Y n -admittance of harmonic index n, computed as specified by the equation and defined by CPC I n , V n -current and voltage harmonics of index n after FFT transformation of timedomain variables G n , B n -Real (active) and imaginary (reactive) admittance components. However, decomposition of "customer current" into harmonics is possible, and decomposition into real and imaginary is possible, if a modification from CPC is made in favor of anomaly detection, as shall be shown at Equation (27). For example, interpretation of current harmonics of wind farms, using CPC may be found in [37].…”

“…If such R, L, C sensitive CPC + CNN models, for example, are effective, then they are suitable for (1) real-time, (2) remote, (3) multi-channel, (4) highly accurate, (5) require less data and faster decision for objectives of (a) preventive maintenance [26][27][28][29], (b) grid anomaly detection, and load extraction through measurement. Such circuit extraction methods are found in scientific branches parallel to electric machinery, such as Lithium battery equivalent schematic [30] deconstruction.…”

“…Much work exists on the analytics module with relative neglect of preprocessing to the AI and data preparation model. The first attempts to use CPC for anomaly detection in smart grids were reported in [26,27]. Using raw data: there is no guarantee that the correct anomalous signal subcluster of highly correlated signals will be selected, thereby reducing the collaborative anomaly detection probability.…”

Joint Feature Selection of Power Load in Time Domain and Frequency Domain Based on Whale Optimization Algorithm

“…en, according to the unique harmonic component of each power load, a feature filter is established to filter the decomposed power flow, so as to realize load identification. Based on the analysis of the actual measured current waveform of grid-connected equipment by power monitor, Beck et al [21] proposed a practical power load identification method. A set of features was extracted by using the current physical component based on power theory decomposition, and the high-precision identification of power load was realized by using artificial neural network and the nearest neighbor search.…”