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Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

Author

Listed:
  • Malgorzata Binek

    (Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego 18/22, 90-924 Lodz, Poland)

  • Andrzej Kanicki

    (Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego 18/22, 90-924 Lodz, Poland)

  • Pawel Rozga

    (Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego 18/22, 90-924 Lodz, Poland)

Abstract

Dynamic phenomena in electric power systems require fast and accurate algorithms for processing signals. The processing results include synchrophasor parameters, e.g., varying amplitude, phase or frequency of sinusoidal voltage or current signals. This paper presents a novel estimation method of synchrophasor parameters that comply with the requirements of IEEE/IEC standards. The authors analyzed an algorithm for measuring the phasor magnitude by means of a selected artificial neural network (ANN), an algorithm for estimating the phasor phase and frequency that makes use of the zero-crossing method. The original components of the presented approach are: the method of the synchrophasor magnitude estimation by means of a suitably trained and applied radial basic function (RBF); the idea of using two algorithms operating simultaneously to estimate the synchrophasor magnitude, phase and frequency that apply identical calculation methods are different in that the first one filters the input signal using the FIR filter and the second one operates without any filter; and the algorithm calculating corrections of the phase shift between the input and output signal and the algorithm calculating corrections of the magnitude estimation. The error results obtained from the applied algorithms were compared with those of the quadrature filter method and the ones presented in literature, as well as with the permissible values of the errors. In all cases, these results were lower than the permissible values and at least equal to the values found in the literature.

Suggested Citation

  • Malgorzata Binek & Andrzej Kanicki & Pawel Rozga, 2021. "Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System," Energies, MDPI, vol. 14(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2570-:d:546689
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    References listed on IDEAS

    as
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    5. Hui Xue & Mengjie Ruan & Yifan Cheng, 2019. "A Fixed Length Adaptive Moving Average Filter-Based Synchrophasor Measurement Algorithm for P Class PMUs," Energies, MDPI, vol. 12(21), pages 1-14, November.
    6. Hui Xue & Yifan Cheng & Mengjie Ruan, 2019. "Enhanced Flat Window-Based Synchrophasor Measurement Algorithm for P Class PMUs," Energies, MDPI, vol. 12(21), pages 1-17, October.
    7. Antonio Delle Femine & Daniele Gallo & Carmine Landi & Mario Luiso, 2019. "The Design of a Low Cost Phasor Measurement Unit," Energies, MDPI, vol. 12(14), pages 1-15, July.
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