IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i13p2508-d244055.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/13/2508/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/13/2508/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Gururajapathy, S.S. & Mokhlis, H. & Illias, H.A., 2017. "Fault location and detection techniques in power distribution systems with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 949-958.
    3. Ahmadipour, Masoud & Hizam, Hashim & Othman, Mohammad Lutfi & Radzi, Mohd Amran Mohd & Murthy, Avinash Srikanta, 2018. "Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 231(C), pages 645-659.
    4. Samet, Haidar & Hashemi, Farid & Ghanbari, Teymoor, 2015. "Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1-18.
    5. Mehdi Baghli & Claude Delpha & Demba Diallo & Abdelhamid Hallouche & David Mba & Tianzhen Wang, 2019. "Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis," Energies, MDPI, vol. 12(7), pages 1-20, April.
    6. Ferdinando Chiacchio & Fabio Famoso & Diego D’Urso & Sebastian Brusca & Jose Ignacio Aizpurua & Luca Cedola, 2018. "Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model," Energies, MDPI, vol. 11(2), pages 1-22, January.
    7. Dashti, Rahman & Ghasemi, Mohsen & Daisy, Mohammad, 2018. "Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model," Energy, Elsevier, vol. 159(C), pages 344-360.
    8. Kong, Xiangrui & Xu, Xiaoyuan & Yan, Zheng & Chen, Sijie & Yang, Huoming & Han, Dong, 2018. "Deep learning hybrid method for islanding detection in distributed generation," Applied Energy, Elsevier, vol. 210(C), pages 776-785.
    9. Colli, Alessandra, 2015. "Failure mode and effect analysis for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 804-809.
    10. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.
    11. Qinyue Zhu & Zhaoyang Li & Xitang Tan & Dabo Xie & Wei Dai, 2019. "Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer," Energies, MDPI, vol. 12(9), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Govind Sahay Yogee & Om Prakash Mahela & Kapil Dev Kansal & Baseem Khan & Rajendra Mahla & Hassan Haes Alhelou & Pierluigi Siano, 2020. "An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 13(9), pages 1-22, May.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. Ananda Kumar & M. S. P. Subathra & Nallapaneni Manoj Kumar & Maria Malvoni & N. J. Sairamya & S. Thomas George & Easter S. Suviseshamuthu & Shauhrat S. Chopra, 2020. "A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network," Energies, MDPI, vol. 13(16), pages 1-22, August.
    2. Khan, Mohammed Ali & Haque, Ahteshamul & Kurukuru, V.S. Bharath & Saad, Mekhilef, 2022. "Islanding detection techniques for grid-connected photovoltaic systems-A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Saeid Khavari & Rahman Dashti & Hamid Reza Shaker & Athila Santos, 2020. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," Energies, MDPI, vol. 13(9), pages 1-15, May.
    4. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.
    5. Stefan Baschel & Elena Koubli & Jyotirmoy Roy & Ralph Gottschalg, 2018. "Impact of Component Reliability on Large Scale Photovoltaic Systems’ Performance," Energies, MDPI, vol. 11(6), pages 1-16, June.
    6. Ming Li & Anqing Chen & Peixiong Liu & Wenbo Ren & Chenghao Zheng, 2024. "Multivariable Algorithm Using Signal-Processing Techniques to Identify Islanding Events in Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 17(4), pages 1-23, February.
    7. Sayed, A. & EL-Shimy, M. & El-Metwally, M. & Elshahed, M., 2020. "Impact of subsystems on the overall system availability for the large scale grid-connected photovoltaic systems," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    8. A. Sayed & M. El-Shimy & M. El-Metwally & M. Elshahed, 2019. "Reliability, Availability and Maintainability Analysis for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 12(7), pages 1-18, March.
    9. Fabrizio Bonacina & Alessandro Corsini & Lucio Cardillo & Francesca Lucchetta, 2019. "Complex Network Analysis of Photovoltaic Plant Operations and Failure Modes," Energies, MDPI, vol. 12(10), pages 1-14, May.
    10. Peters, Lennart & Madlener, Reinhard, 2017. "Economic evaluation of maintenance strategies for ground-mounted solar photovoltaic plants," Applied Energy, Elsevier, vol. 199(C), pages 264-280.
    11. Mingzhen Li & Jialong Bu & Yupeng Song & Zhongyi Pu & Yuli Wang & Cheng Xie, 2021. "A Novel Fault Location Method for Power Cables Based on an Unsupervised Learning Algorithm," Energies, MDPI, vol. 14(4), pages 1-19, February.
    12. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    13. Lisa B. Bosman & Walter D. Leon-Salas & William Hutzel & Esteban A. Soto, 2020. "PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities," Energies, MDPI, vol. 13(6), pages 1-16, March.
    14. Zhang, Ziqi & Li, Peng & Ji, Haoran & Zhao, Jinli & Xi, Wei & Wu, Jianzhong & Wang, Chengshan, 2024. "Combined central-local voltage control of inverter-based DG in active distribution networks11The short version of the paper was presented at CUE2023. This paper is a substantial extension of the short," Applied Energy, Elsevier, vol. 372(C).
    15. Sajjad Bahrebar & Frede Blaabjerg & Huai Wang & Navid Vafamand & Mohammad-Hassan Khooban & Sima Rastayesh & Dao Zhou, 2018. "A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application," Energies, MDPI, vol. 11(4), pages 1-16, March.
    16. Gong, Bin & An, Aimin & Shi, Yaoke & Guan, Haijiao & Jia, Wenchao & Yang, Fazhi, 2024. "An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction," Energy, Elsevier, vol. 308(C).
    17. 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.
    18. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.
    19. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    20. Zhou, Hao & Yang, Hongxing & Peng, Jinqing, 2024. "Solar PV vacuum glazing (SVG) insulated building facades: Thermal and electrical performances," Applied Energy, Elsevier, vol. 376(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2508-:d:244055. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.