IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v68y2017ip1p102-112.html
   My bibliography  Save this article

Evaluation of power transformer inrush currents and internal faults discrimination methods in presence of fault current limiter

Author

Listed:
  • Sahebi, Ali
  • Samet, Haidar
  • Ghanbari, Teymoor

Abstract

Due to increase in penetration of Distributed Generations (DGs) in power systems, fault current level is being increased, which results in some problems in the systems. Fault Current Limiters (FCLs) are attractive devices to tackle these problems for transmission and distribution systems. The utilized FCLs may have considerable impact on the signals used for differential protection of power transformers, which leads to mal-operation of these protections. It seems a comprehensive analysis is necessary for performance evaluation of differential protection algorithms in presence of FCLs. This paper deals with investigation of FCLs impact on power transformers’ differential protection. The performance of some well-known differential protection algorithms for discrimination between internal fault current and magnetizing inrush current with and without presence of FCL are evaluated.

Suggested Citation

  • Sahebi, Ali & Samet, Haidar & Ghanbari, Teymoor, 2017. "Evaluation of power transformer inrush currents and internal faults discrimination methods in presence of fault current limiter," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 102-112.
  • Handle: RePEc:eee:rensus:v:68:y:2017:i:p1:p:102-112
    DOI: 10.1016/j.rser.2016.09.124
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116306293
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2016.09.124?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    2. Taghavi, Reza & Seifi, Ali Reza & Samet, Haidar, 2015. "Stochastic reactive power dispatch in hybrid power system with intermittent wind power generation," Energy, Elsevier, vol. 89(C), pages 511-518.
    3. 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.
    4. Samet, Haidar & Marzbani, Fatemeh, 2014. "Quantizing the deterministic nonlinearity in wind speed time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1143-1154.
    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. Guo, Qi & Xiao, Fan & Tu, Chunming & Jiang, Fei & Zhu, Rongwu & Ye, Jian & Gao, Jiayuan, 2022. "An overview of series-connected power electronic converter with function extension strategies in the context of high-penetration of power electronics and renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Md Shafiul Alam & Mohammad Ali Yousef Abido & Ibrahim El-Amin, 2018. "Fault Current Limiters in Power Systems: A Comprehensive Review," Energies, MDPI, vol. 11(5), pages 1-24, April.

    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. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    2. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    3. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    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. Feng, Cong & Sun, Mucun & Cui, Mingjian & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "Characterizing forecastability of wind sites in the United States," Renewable Energy, Elsevier, vol. 133(C), pages 1352-1365.
    6. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    7. Ji, Ling & Huang, Guo-He & Huang, Lu-Cheng & Xie, Yu-Lei & Niu, Dong-Xiao, 2016. "Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty," Energy, Elsevier, vol. 109(C), pages 920-932.
    8. Hugo Tavares Vieira Gouveia & Ronaldo Ribeiro Barbosa De Aquino & Aida Araújo Ferreira, 2018. "Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks," Energies, MDPI, vol. 11(4), pages 1-19, April.
    9. Anaya, K. & Pollitt, M., 2018. "Reactive Power Procurement: Lessons from Three Leading Countries," Cambridge Working Papers in Economics 1854, Faculty of Economics, University of Cambridge.
    10. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(C).
    11. Tsai, Sang-Bing & Xue, Youzhi & Zhang, Jianyu & Chen, Quan & Liu, Yubin & Zhou, Jie & Dong, Weiwei, 2017. "Models for forecasting growth trends in renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1169-1178.
    12. 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).
    13. Tsai, Sang-Bing, 2018. "Using the DEMATEL model to explore the job satisfaction of research and development professionals in china's photovoltaic cell industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 62-68.
    14. M. A. Graña-López & A. García-Diez & A. Filgueira-Vizoso & J. Chouza-Gestoso & A. Masdías-Bonome, 2019. "Study of the Sustainability of Electrical Power Systems: Analysis of the Causes that Generate Reactive Power," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    15. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    16. 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.
    17. Xinxin Zheng & Rui Zhang & Xi Chen & Nong Sun, 2018. "Improved Three-Phase AFD Islanding Detection Based on Digital Control and Non-Detection Zone Elimination," Energies, MDPI, vol. 11(9), pages 1-15, September.
    18. Ibrahim, Thamir k. & Mohammed, Mohammed Kamil & Awad, Omar I. & Rahman, M.M. & Najafi, G. & Basrawi, Firdaus & Abd Alla, Ahmed N. & Mamat, Rizalman, 2017. "The optimum performance of the combined cycle power plant: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 459-474.
    19. Sharma, Akanksha & Jain, Sanjay K., 2021. "Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system," Energy, Elsevier, vol. 223(C).
    20. Balamurugan, M. & Sahoo, Sarat Kumar & Sukchai, Sukruedee, 2017. "Application of soft computing methods for grid connected PV system: A technological and status review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1493-1508.

    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:eee:rensus:v:68:y:2017:i:p1:p:102-112. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.