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Real Fault Location in a Distribution Network Using Smart Feeder Meter Data

Author

Listed:
  • Hamid Mirshekali

    (Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169113817, Iran)

  • Rahman Dashti

    (Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169113817, Iran)

  • Karsten Handrup

    (Kamstrup A/S, Industrivej 28, DK-8660 Stilling, Skanderborg, Denmark)

  • Hamid Reza Shaker

    (Center for Energy Informatics, University of Southern Denmark, DK-5230 Odense, Denmark)

Abstract

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

Suggested Citation

  • Hamid Mirshekali & Rahman Dashti & Karsten Handrup & Hamid Reza Shaker, 2021. "Real Fault Location in a Distribution Network Using Smart Feeder Meter Data," Energies, MDPI, vol. 14(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3242-:d:567290
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    References listed on IDEAS

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    1. Yangang Shi & Tao Zheng & Chang Yang, 2020. "Reflected Traveling Wave Based Single-Ended Fault Location in Distribution Networks," Energies, MDPI, vol. 13(15), pages 1-19, July.
    2. Md Shafiullah & M. A. Abido & Taher Abdel-Fattah, 2018. "Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach," Energies, MDPI, vol. 11(9), pages 1-23, September.
    3. Rui Liang & Zhi Yang & Nan Peng & Chenglei Liu & Firuz Zare, 2017. "Asynchronous Fault Location in Transmission Lines Considering Accurate Variation of the Ground-Mode Traveling Wave Velocity," Energies, MDPI, vol. 10(12), pages 1-18, November.
    4. Chenyu Zhang & Xiaodong Yuan & Mingming Shi & Jinggang Yang & Huiyu Miao, 2020. "Fault Location Method Based on SVM and Similarity Model Matching," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, September.
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    Citations

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    Cited by:

    1. Hamid Mirshekali & Athila Q. Santos & Hamid Reza Shaker, 2023. "A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids," Energies, MDPI, vol. 16(17), pages 1-29, August.
    2. Mohammad Reza Shadi & Hamid Mirshekali & Rahman Dashti & Mohammad-Taghi Ameli & Hamid Reza Shaker, 2021. "A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit," Energies, MDPI, vol. 14(19), pages 1-15, October.
    3. Bartosz Olejnik & Beata Zięba, 2022. "Improving the Efficiency of Earth Fault Detection by Fault Current Passage Indicators in Medium-Voltage Compensated Overhead Networks," Energies, MDPI, vol. 15(23), pages 1-19, November.
    4. Denis Ustinov & Aleksander Nazarychev & Denis Pelenev & Kirill Babyr & Andrey Pugachev, 2023. "Investigation of the Effect of Current Protections in Conditions of Single-Phase Ground Fault through Transient Resistance in the Electrical Networks of Mining Enterprises," Energies, MDPI, vol. 16(9), pages 1-15, April.
    5. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.

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