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Fault Detection in DC Microgrids Using Short-Time Fourier Transform

Author

Listed:
  • Ivan Grcić

    (Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia)

  • Hrvoje Pandžić

    (Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia)

  • Damir Novosel

    (Quanta Technology, Raleigh, NC 27607, USA)

Abstract

Fault detection in microgrids presents a strong technical challenge due to the dynamic operating conditions. Changing the power generation and load impacts the current magnitude and direction, which has an adverse effect on the microgrid protection scheme. To address this problem, this paper addresses a field-transform-based fault detection method immune to the microgrid conditions. The faults are simulated via a Matlab/Simulink model of the grid-connected photovoltaics-based DC microgrid with battery energy storage. Short-time Fourier transform is applied to the fault time signal to obtain a frequency spectrum. Selected spectrum features are then provided to a number of intelligent classifiers. The classifiers’ scores were evaluated using the F1-score metric. Most classifiers proved to be reliable as their performance score was above 90%.

Suggested Citation

  • Ivan Grcić & Hrvoje Pandžić & Damir Novosel, 2021. "Fault Detection in DC Microgrids Using Short-Time Fourier Transform," Energies, MDPI, vol. 14(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:277-:d:475723
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    References listed on IDEAS

    as
    1. Brearley, Belwin J. & Prabu, R. Raja, 2017. "A review on issues and approaches for microgrid protection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 988-997.
    2. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
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    Cited by:

    1. Sirus Salehimehr & Seyed Mahdi Miraftabzadeh & Morris Brenna, 2024. "A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
    2. Maria Fotopoulou & Dimitrios Rakopoulos & Dimitrios Trigkas & Fotis Stergiopoulos & Orestis Blanas & Spyros Voutetakis, 2021. "State of the Art of Low and Medium Voltage Direct Current (DC) Microgrids," Energies, MDPI, vol. 14(18), pages 1-27, September.

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