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Harmonic Loads Classification by Means of Currents’ Physical Components

Author

Listed:
  • Yuval Beck

    (The Physical Electronics Department, School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel)

  • Ram Machlev

    (The Physical Electronics Department, School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel)

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.

Suggested Citation

  • Yuval Beck & Ram Machlev, 2019. "Harmonic Loads Classification by Means of Currents’ Physical Components," Energies, MDPI, vol. 12(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4137-:d:281697
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    References listed on IDEAS

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    Cited by:

    1. Netzah Calamaro & Avihai Ofir & Doron Shmilovitz, 2021. "Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation," Energies, MDPI, vol. 14(11), pages 1-41, June.

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