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High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

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  • Veerapandiyan Veerasamy

    (Advanced Lightning and Power Energy System (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), UPM Serdang 43400, Selangor, Malaysia)

  • Noor Izzri Abdul Wahab

    (Advanced Lightning and Power Energy System (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), UPM Serdang 43400, Selangor, Malaysia)

  • Rajeswari Ramachandran

    (Department of Electrical Engineering, Government College of Technology, Coimbatore 641013, Tamilnadu, India)

  • Muhammad Mansoor

    (Advanced Lightning and Power Energy System (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), UPM Serdang 43400, Selangor, Malaysia
    Pakistan Institute of Engineering and Technology, Multan 59060, Pakistan)

  • Mariammal Thirumeni

    (Department of Electrical Engineering, Rajalakshmi Engineering College, Chennai 602105, Tamilnadu, India)

  • Mohammad Lutfi Othman

    (Advanced Lightning and Power Energy System (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), UPM Serdang 43400, Selangor, Malaysia)

Abstract

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

Suggested Citation

  • Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3330-:d:186390
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    References listed on IDEAS

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    Cited by:

    1. Francinei L. Vieira & Pedro H. M. Santos & José M. Carvalho Filho & Roberto C. Leborgne & Marino P. Leite, 2019. "A Voltage-Based Approach for Series High Impedance Fault Detection and Location in Distribution Systems Using Smart Meters," Energies, MDPI, vol. 12(15), pages 1-16, August.
    2. Yu He & Xinhui Zhang & Wenhao Wu & Jun Zhang & Wenyuan Bai & Aiyu Guo & Yu Chen, 2022. "Faulty Line Selection Method Based on Comprehensive Dynamic Time Warping Distance in a Flexible Grounding System," Energies, MDPI, vol. 15(2), pages 1-16, January.
    3. Elhadi Aker & Mohammad Lutfi Othman & Veerapandiyan Veerasamy & Ishak bin Aris & Noor Izzri Abdul Wahab & Hashim Hizam, 2020. "Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier," Energies, MDPI, vol. 13(1), pages 1-24, January.

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