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A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

Author

Listed:
  • Masoud Ahmadipour

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
    Advanced Lightning and Power Energy Research (ALPER), Universiti Putra Malaysia, Selangor 43400, Malaysia)

  • Hashim Hizam

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
    Advanced Lightning and Power Energy Research (ALPER), Universiti Putra Malaysia, Selangor 43400, Malaysia)

  • Mohammad Lutfi Othman

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
    Advanced Lightning and Power Energy Research (ALPER), Universiti Putra Malaysia, Selangor 43400, Malaysia)

  • Mohd Amran Mohd Radzi

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
    Advanced Lightning and Power Energy Research (ALPER), Universiti Putra Malaysia, Selangor 43400, Malaysia)

  • Nikta Chireh

    (Department of Architecture Engineering, Alaodoleh Semnani Institute of Higher Education, Garmsar 33335815, Semnan, Iran)

Abstract

A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

Suggested Citation

  • Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2508-:d:244055
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    References listed on IDEAS

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    Cited by:

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    2. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A Novel Fault Detection and Classification Strategy for Photovoltaic Distribution Network Using Improved Hilbert–Huang Transform and Ensemble Learning Technique," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    3. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach," Energies, MDPI, vol. 15(20), pages 1-20, October.
    4. Kuei-Hsiang Chao & Chen-Hou Ke, 2020. "Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives," Energies, MDPI, vol. 13(23), pages 1-25, November.

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