IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5180-d423910.html
   My bibliography  Save this article

Islanding Detection Using a Micro-Synchrophasor for Distribution Systems with Distributed Generation

Author

Listed:
  • Karthikeyan Subramanian

    (Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India)

  • Ashok Kumar Loganathan

    (Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India)

Abstract

Distributed Generation (DG) has changed the power generation system to small-scale instead of large-scale generation. The demanding issue with the interconnection of DG is the detection of unintended islanding in a network. Several methods proposed in the literature show drawbacks such as high non-detection zones (NDZ) and higher tripping time. In this paper, the IEEE 13 bus distribution network with DGs like wind and solar power plants is integrated at two buses. Islanding is detected by utilizing data from a micro-synchrophasor located at the distribution grid and the DG. The micro-synchrophasor-based unintended islanding detection algorithm is based on parameters such as voltage, rate of change of voltage, frequency, rate of change of frequency, voltage phase angle difference and the rate of change of the voltage phase angle difference between the utility and the islanded grid. The proposed islanding detection algorithm discriminates between islanding and non-islanding conditions and is highly efficient under zero power mismatch conditions. The proposed method has null NDZ and satisfies the IEEE 1547 standard for DG tripping time. The effectiveness of the proposed IDM was verified when there are multiple DGs in the islanded grid. Also, the proposed method does not require additional hardware as it can be incorporated in digital relays with synchrophasor functionality.

Suggested Citation

  • Karthikeyan Subramanian & Ashok Kumar Loganathan, 2020. "Islanding Detection Using a Micro-Synchrophasor for Distribution Systems with Distributed Generation," Energies, MDPI, vol. 13(19), pages 1-31, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5180-:d:423910
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5180/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/19/5180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reza Zamani & Mohamad-Esmail Hamedani-Golshan & Hassan Haes Alhelou & Pierluigi Siano & Hemanshu R. Pota, 2018. "Islanding Detection of Synchronous Distributed Generator Based on the Active and Reactive Power Control Loops," Energies, MDPI, vol. 11(10), pages 1-15, October.
    2. Kuang-Hsiung Tan & Chien-Wu Lan, 2019. "DG System Using PFNN Controllers for Improving Islanding Detection and Power Control," Energies, MDPI, vol. 12(3), pages 1-19, February.
    3. Fatemeh Ghalavand & Behzad Asle Mohammadi Alizade & Hossam Gaber & Hadis Karimipour, 2018. "Microgrid Islanding Detection Based on Mathematical Morphology," Energies, MDPI, vol. 11(10), pages 1-18, October.
    4. Bayrak, Gökay & Kabalci, Ersan, 2016. "Implementation of a new remote islanding detection method for wind–solar hybrid power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1-15.
    5. Mojgan Hojabri & Ulrich Dersch & Antonios Papaemmanouil & Peter Bosshart, 2019. "A Comprehensive Survey on Phasor Measurement Unit Applications in Distribution Systems," Energies, MDPI, vol. 12(23), pages 1-23, November.
    6. Taiying Zheng & Huan Yang & Rongxiang Zhao & Yong Cheol Kang & Vladimir Terzija, 2018. "Design, Evaluation and Implementation of an Islanding Detection Method for a Micro-grid," Energies, MDPI, vol. 11(2), pages 1-24, February.
    7. Min-Sung Kim & Raza Haider & Gyu-Jung Cho & Chul-Hwan Kim & Chung-Yuen Won & Jong-Seo Chai, 2019. "Comprehensive Review of Islanding Detection Methods for Distributed Generation Systems," Energies, MDPI, vol. 12(5), pages 1-21, March.
    8. 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.
    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. Ênio Costa Resende & Henrique Tannús de Moura Carvalho & Luiz Carlos Gomes Freitas, 2022. "Implementation and Critical Analysis of the Active Phase Jump with Positive Feedback Anti-Islanding Algorithm," Energies, MDPI, vol. 15(13), pages 1-27, June.
    2. Juan Roberto Lopez & Luis Ibarra & Pedro Ponce & Arturo Molina, 2021. "A Decentralized Passive Islanding Detection Method Based on the Variations of Estimated Droop Characteristics," Energies, MDPI, vol. 14(22), pages 1-19, November.
    3. Szymon Barczentewicz & Tomasz Lerch & Andrzej Bień & Krzysztof Duda, 2021. "Laboratory Evaluation of a Phasor-Based Islanding Detection Method," Energies, MDPI, vol. 14(7), pages 1-17, 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. Mazaher Karimi & Mohammad Farshad & Qiteng Hong & Hannu Laaksonen & Kimmo Kauhaniemi, 2020. "An Islanding Detection Technique for Inverter-Based Distributed Generation in Microgrids," Energies, MDPI, vol. 14(1), pages 1-18, December.
    2. Arash Abyaz & Habib Panahi & Reza Zamani & Hassan Haes Alhelou & Pierluigi Siano & Miadreza Shafie-khah & Mimmo Parente, 2019. "An Effective Passive Islanding Detection Algorithm for Distributed Generations," Energies, MDPI, vol. 12(16), pages 1-19, August.
    3. 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.
    4. Faisal Mumtaz & Kashif Imran & Abdullah Abusorrah & Syed Basit Ali Bukhari, 2023. "An Extensive Overview of Islanding Detection Strategies of Active Distributed Generations in Sustainable Microgrids," Sustainability, MDPI, vol. 15(5), pages 1-19, March.
    5. Phylicia Cicilio & David Glennon & Adam Mate & Arthur Barnes & Vishvas Chalishazar & Eduardo Cotilla-Sanchez & Bjorn Vaagensmith & Jake Gentle & Craig Rieger & Richard Wies & Mohammad Heidari Kapourch, 2021. "Resilience in an Evolving Electrical Grid," Energies, MDPI, vol. 14(3), pages 1-25, January.
    6. Min-Sung Kim & Raza Haider & Gyu-Jung Cho & Chul-Hwan Kim & Chung-Yuen Won & Jong-Seo Chai, 2019. "Comprehensive Review of Islanding Detection Methods for Distributed Generation Systems," Energies, MDPI, vol. 12(5), pages 1-21, March.
    7. Jihed Hmad & Azeddine Houari & Allal El Moubarek Bouzid & Abdelhakim Saim & Hafedh Trabelsi, 2023. "A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-41, June.
    8. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    9. Boscaino, Valeria & Ditta, Vito & Marsala, Giuseppe & Panzavecchia, Nicola & Tinè, Giovanni & Cosentino, Valentina & Cataliotti, Antonio & Di Cara, Dario, 2024. "Grid-connected photovoltaic inverters: Grid codes, topologies and control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    10. Qolipour, Mojtaba & Mostafaeipour, Ali & Tousi, Omid Mohseni, 2017. "Techno-economic feasibility of a photovoltaic-wind power plant construction for electric and hydrogen production: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 113-123.
    11. Chinmayee Biswal & Binod Kumar Sahu & Manohar Mishra & Pravat Kumar Rout, 2023. "Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units," Energies, MDPI, vol. 16(10), pages 1-34, May.
    12. Marino Coppola & Pierluigi Guerriero & Adolfo Dannier & Santolo Daliento & Davide Lauria & Andrea Del Pizzo, 2020. "Control of a Fault-Tolerant Photovoltaic Energy Converter in Island Operation," Energies, MDPI, vol. 13(12), pages 1-18, June.
    13. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    14. David Granados-Lieberman, 2020. "Global Harmonic Parameters for Estimation of Power Quality Indices: An Approach for PMUs," Energies, MDPI, vol. 13(9), pages 1-17, May.
    15. Antonio Rosales & Pedro Ponce & Hiram Ponce & Arturo Molina, 2019. "A Robust Control Scheme for Renewable-Based Distributed Generators Using Artificial Hydrocarbon Networks," Energies, MDPI, vol. 12(10), pages 1-18, May.
    16. 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.
    17. Jing Zhang & Yiqi Li & Zhi Wu & Chunyan Rong & Tao Wang & Zhang Zhang & Suyang Zhou, 2021. "Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems," Energies, MDPI, vol. 14(12), pages 1-15, June.
    18. Do-In Kim, 2021. "Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network," Energies, MDPI, vol. 14(15), pages 1-15, July.
    19. Szymon Barczentewicz & Tomasz Lerch & Andrzej Bień & Krzysztof Duda, 2021. "Laboratory Evaluation of a Phasor-Based Islanding Detection Method," Energies, MDPI, vol. 14(7), pages 1-17, April.
    20. Nitin Kumar Kulkarni & Mohan Khedkar & Chandrashekhar Narayan Bhende & Sunil Kumar Singh, 2022. "Prioritization of Passive Parameters from Modified Averaging Approach-Based Computation (MAAC) Methodology for Ascertaining Formation of Single and Multi-Location Unintentional Islands," Energies, MDPI, vol. 15(17), pages 1-25, September.

    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:19:p:5180-:d:423910. 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.