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Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Author

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  • Hsueh-Hsien Chang

    (Department of Electronic Engineering, JinWen University of Science and Technology, New Taipei 23154, Taiwan)

  • Nguyen Viet Linh

    (Department of Electrical, Electronic, Computers and Systems Engineering, University of Oviedo, Oviedo 33003, Spain)

Abstract

This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:611-:d:97203
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    References listed on IDEAS

    as
    1. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
    2. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    3. Kofi Afrifa Agyeman & Sekyung Han & Soohee Han, 2015. "Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System," Energies, MDPI, vol. 8(9), pages 1-20, August.
    4. Chang, Hsueh-Hsien, 2011. "Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units," Energy, Elsevier, vol. 36(1), pages 181-190.
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    Cited by:

    1. 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.
    2. Hong-Keun Ji & Guoming Wang & Woo-Hyun Kim & Gyung-Suk Kil, 2018. "Optimal Design of a Band Pass Filter and an Algorithm for Series Arc Detection," Energies, MDPI, vol. 11(4), pages 1-13, April.
    3. Hong-Keun Ji & Guoming Wang & Gyung-Suk Kil, 2020. "Optimal Detection and Identification of DC Series Arc in Power Distribution System on Shipboards," Energies, MDPI, vol. 13(22), pages 1-16, November.
    4. Zhuo Liu & Tianzhen Wang & Tianhao Tang & Yide Wang, 2017. "A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI," Energies, MDPI, vol. 10(9), pages 1-15, August.
    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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