IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i9p2383-d356133.html
   My bibliography  Save this article

An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration

Author

Listed:
  • Govind Sahay Yogee

    (Department of Electrical Engineering, Jaipur Institute of Technology, Jaipur 302037, India)

  • Om Prakash Mahela

    (Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, India)

  • Kapil Dev Kansal

    (Department of Electrical Engineering, Jaipur Institute of Technology, Jaipur 302037, India)

  • Baseem Khan

    (Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano (SA), Italy
    Department of Electrical and Computer Engineering, Hawassa University, Hawassa 05, Ethiopia)

  • Rajendra Mahla

    (Department of Electrical Engineering, National Institute of Technology, Kurukshetra 136119, India)

  • Hassan Haes Alhelou

    (Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano (SA), Italy
    Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria)

  • Pierluigi Siano

    (Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano (SA), Italy)

Abstract

Penetration level of renewable energy (RE) in the utility grid is continuously increasing to minimize the environmental concerns, risk of energy security, and depletion of fossil fuels. The uncertain nature and availability of RE power for a short duration have created problems related to the protection, grid security, power reliability, and power quality. Further, integration of RE sources near the load centers has also pronounced the protection issues, such as false tripping, delayed tripping, etc. Hence, this paper introduces a hybrid grid protection scheme (HGPS) for the protection of the grid with RE integration. This combines the merits of the Stockwell Transform, Hilbert Transform, and Alienation Coefficient to improve performance of the protection scheme. The Stockwell Transform-based Median and Summation Index (SMSI) utilizing current signals, Hilbert Transform-based derivative index (HDI) utilizing voltage signals, and Alienation Coefficient index (ACI) utilizing voltage signals were used to compute a proposed Stockwell Transform-, Hilbert Transform-, and Alienation-based fault index (SAHFI). This SAHFI was used to recognize the fault conditions. The fault conditions were categorized using the number of faulty phases and the proposed Stockwell Transform and Hilbert Transform-based ground fault index (SHGFI) utilizing zero sequence currents. The fault conditions, such as phase and ground (PGF), any two phases (TPF), any two phases and ground (TPGF), all three phases (ATPF), and all three phases and ground (ATPGF), were recognized effectively, using the proposed SAHFI. The proposed method has the following merits: performance is least affected by the noise, it is effective in recognizing fault conditions in minimum time, and it is also effective in recognizing the fault conditions in different scenarios of the grid. Performance of the proposed approach was found to be superior compared to the discrete wavelet transform (DWT)-based method reported in the literature. The study was performed using the hybrid grid test system realized by integrating wind and solar photovoltaic (PV) plants to the IEEE-13 nodes network in MATLAB software.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2383-:d:356133
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/9/2383/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/9/2383/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2017. "Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering," Renewable Energy, Elsevier, vol. 106(C), pages 37-51.
    2. Cheng-I Chen & Chien-Kai Lan & Yeong-Chin Chen & Chung-Hsien Chen & Yung-Ruei Chang, 2020. "Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid," Energies, MDPI, vol. 13(4), pages 1-13, February.
    3. Barra, P.H.A. & Coury, D.V. & Fernandes, R.A.S., 2020. "A survey on adaptive protection of microgrids and distribution systems with distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    4. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    5. 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.
    6. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2016. "Comprehensive overview of grid interfaced wind energy generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 260-281.
    7. Sheesh Ram Ola & Amit Saraswat & Sunil Kumar Goyal & Virendra Sharma & Baseem Khan & Om Prakash Mahela & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Alienation Coefficient and Wigner Distribution Function Based Protection Scheme for Hybrid Power System Network with Renewable Energy Penetration," Energies, MDPI, vol. 13(5), pages 1-25, March.
    8. Wanghao Fei & Paul Moses, 2019. "Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks," Energies, MDPI, vol. 12(22), pages 1-12, November.
    9. Eissa (SIEEE), M.M., 2015. "Protection techniques with renewable resources and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1645-1667.
    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. Mostafa Bakkar & Santiago Bogarra & Felipe Córcoles & Ahmed Aboelhassan & Shuo Wang & Javier Iglesias, 2022. "Artificial Intelligence-Based Protection for Smart Grids," Energies, MDPI, vol. 15(13), pages 1-18, July.
    2. Miguel Louro & Luís Ferreira, 2021. "Underground MV Network Failures’ Waveform Characteristics—An Investigation," Energies, MDPI, vol. 14(5), pages 1-14, February.

    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. Sheesh Ram Ola & Amit Saraswat & Sunil Kumar Goyal & Virendra Sharma & Baseem Khan & Om Prakash Mahela & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Alienation Coefficient and Wigner Distribution Function Based Protection Scheme for Hybrid Power System Network with Renewable Energy Penetration," Energies, MDPI, vol. 13(5), pages 1-25, March.
    2. Barra, P.H.A. & de Carvalho, W.C. & Menezes, T.S. & Fernandes, R.A.S. & Coury, D.V., 2021. "A review on wind power smoothing using high-power energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    3. Atul Kulshrestha & Om Prakash Mahela & Mukesh Kumar Gupta & Neeraj Gupta & Nilesh Patel & Tomonobu Senjyu & Mir Sayed Shah Danish & Mahdi Khosravy, 2020. "A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration," Energies, MDPI, vol. 13(14), pages 1-25, July.
    4. Ji Han & Li Li & Huihui Song & Meng Liu & Zongxun Song & Yanbin Qu, 2022. "An Equivalent Model of Wind Farm Based on Multivariate Multi-Scale Entropy and Multi-View Clustering," Energies, MDPI, vol. 15(16), pages 1-17, August.
    5. Shah Rukh Abbas & Syed Ali Abbas Kazmi & Muhammad Naqvi & Adeel Javed & Salman Raza Naqvi & Kafait Ullah & Tauseef-ur-Rehman Khan & Dong Ryeol Shin, 2020. "Impact Analysis of Large-Scale Wind Farms Integration in Weak Transmission Grid from Technical Perspectives," Energies, MDPI, vol. 13(20), pages 1-32, October.
    6. Mohamed Zribi & Muthana Alrifai & Mohamed Rayan, 2017. "Sliding Mode Control of a Variable- Speed Wind Energy Conversion System Using a Squirrel Cage Induction Generator," Energies, MDPI, vol. 10(5), pages 1-21, May.
    7. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    8. Kalair, A. & Abas, N. & Khan, N., 2016. "Comparative study of HVAC and HVDC transmission systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1653-1675.
    9. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    10. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    11. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    12. 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.
    13. Rafael Sebastián, 2021. "Review on Dynamic Simulation of Wind Diesel Isolated Microgrids," Energies, MDPI, vol. 14(7), pages 1-17, March.
    14. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    15. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    16. Yijin Li & Jianhua Lin & Geng Niu & Ming Wu & Xuteng Wei, 2021. "A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids," Energies, MDPI, vol. 14(16), pages 1-16, August.
    17. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2019. "Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    18. Pandelara, Diego & Kristjanpoller, Werner & Michell, Kevin & Minutolo, Marcel C., 2022. "A fuzzy regression causality approach to analyze relationship between electrical consumption and GDP," Energy, Elsevier, vol. 239(PE).
    19. 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.
    20. Alex Valenzuela & Silvio Simani & Esteban Inga, 2021. "Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication," Energies, MDPI, vol. 14(11), pages 1-22, June.

    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:13:y:2020:i:9:p:2383-:d:356133. 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.