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A Novel Method for Obtaining the Signature of Household Consumer Pairs

Author

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  • Dadiana-Valeria Căiman

    (Faculty of Automation and Applied Informatics, Politehnica University Timișoara, 300223 Timișoara, Romania)

  • Toma-Leonida Dragomir

    (Faculty of Automation and Applied Informatics, Politehnica University Timișoara, 300223 Timișoara, Romania)

Abstract

The management of electricity consumption by household consumers requires multiple ways of consumer monitoring. One of these is the signature i ( v ) determined by monitoring the consumer voltage-current trajectory. The paper proposes a novel method for obtaining signatures of 2-multiple consumers, i.e., a pair of consumers connected in parallel. Signatures are obtained from samples of the voltage at the consumers’ terminals and of the total current absorbed by the consumers, measured at a frequency of only 20 Hz. Within the method, signatures are calculated using genetic algorithms (GA) and nonlinear regression, according to a procedure developed by the authors in a previous paper. The management of the data selected for the signature assignment represents the novelty. The method proposed in this paper is applied in two case studies, one concerning household consumers within the same power level, the other for household consumers of different power levels. The results confirm the possibility of obtaining signatures of i ( v ) type.

Suggested Citation

  • Dadiana-Valeria Căiman & Toma-Leonida Dragomir, 2020. "A Novel Method for Obtaining the Signature of Household Consumer Pairs," Energies, MDPI, vol. 13(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6030-:d:447165
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    References listed on IDEAS

    as
    1. Darío Baptista & Sheikh Shanawaz Mostafa & Lucas Pereira & Leonel Sousa & Fernando Morgado-Dias, 2018. "Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory," Energies, MDPI, vol. 11(9), pages 1-18, September.
    2. Anthony Faustine & Lucas Pereira, 2020. "Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks," Energies, MDPI, vol. 13(13), pages 1-15, July.
    3. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    4. Anthony Faustine & Lucas Pereira, 2020. "Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 13(16), pages 1-17, August.
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