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Location of Faults in Power Transmission Lines Using the ARIMA Method

Author

Listed:
  • Danilo Pinto Moreira de Souza

    (Computational Modeling in Science and Technology (MCCT), Fluminense Federal University (UFF), Volta Redonda 21941-916, Brazil)

  • Eliane Da Silva Christo

    (Computational Modeling in Science and Technology (MCCT), Fluminense Federal University (UFF), Volta Redonda 21941-916, Brazil)

  • Aryfrance Rocha Almeida

    (Technology Center, Federal University of Piauí (UFPI), Teresina 60455-760, Brazil)

Abstract

One of the major problems in transmission lines is the occurrence of failures that affect the quality of the electric power supplied, as the exact localization of the fault must be known for correction. In order to streamline the work of maintenance teams and standardize services, this paper proposes a method of locating faults in power transmission lines by analyzing the voltage oscillographic signals extracted at the line monitoring terminals. The developed method relates time series models obtained specifically for each failure pattern. The parameters of the autoregressive integrated moving average ( ARIMA ) model are estimated in order to adjust the voltage curves and calculate the distance from the initial fault localization to the terminals. Simulations of the failures are performed through the ATPDraw ® (5.5) software and the analyses were completed using the RStudio ® (1.0.143) software. The results obtained with respect to the failures, which did not involve earth return, were satisfactory when compared with widely used techniques in the literature, particularly when the fault distance became larger in relation to the beginning of the transmission line.

Suggested Citation

  • Danilo Pinto Moreira de Souza & Eliane Da Silva Christo & Aryfrance Rocha Almeida, 2017. "Location of Faults in Power Transmission Lines Using the ARIMA Method," Energies, MDPI, vol. 10(10), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1596-:d:114902
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Enrique Personal & Antonio García & Antonio Parejo & Diego Francisco Larios & Félix Biscarri & Carlos León, 2016. "A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems," Energies, MDPI, vol. 9(12), pages 1-30, December.
    3. Hsueh-Hsien Chang & Nguyen Viet Linh, 2017. "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems," Energies, MDPI, vol. 10(5), pages 1-20, April.
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    Cited by:

    1. Jindamas Sutthichaimethee & Kuskana Kubaha, 2018. "Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    2. Pedro Faria, 2019. "Distributed Energy Resources Management," Energies, MDPI, vol. 12(3), pages 1-3, February.
    3. Zuzana Bukvisova & Jaroslava Orsagova & David Topolanek & Petr Toman, 2019. "Two-Terminal Algorithm Analysis for Unsymmetrical Fault Location on 110 kV Lines," Energies, MDPI, vol. 12(7), pages 1-14, March.
    4. Pulin Cao & Hongchun Shu & Bo Yang & Na An & Dalin Qiu & Weiye Teng & Jun Dong, 2018. "Voltage Distribution–Based Fault Location for Half-Wavelength Transmission Line with Large-Scale Wind Power Integration in China," Energies, MDPI, vol. 11(3), pages 1-22, March.
    5. Yi Ning & Dazhi Wang & Yunlu Li & Haixin Zhang, 2018. "Location of Faulty Section and Faults in Hybrid Multi-Terminal Lines Based on Traveling Wave Methods," Energies, MDPI, vol. 11(5), pages 1-18, May.

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