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A Decentralized Passive Islanding Detection Method Based on the Variations of Estimated Droop Characteristics

Author

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  • Juan Roberto Lopez

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

  • Luis Ibarra

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

  • Pedro Ponce

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

  • Arturo Molina

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

Abstract

A microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation . The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes a straightforward, distributed island detection technique only relying on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A “stiffness change” effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7759-:d:682754
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    References listed on IDEAS

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    1. Ahmed Amirul Arefin & Khairul Nisak Binti Md. Hasan & Mohammad Lutfi Othman & Mohd Fakhizan Romlie & Nordin Saad & Nursyarizal Bin Mohd Nor & Mohd Faris Abdullah, 2021. "A Novel Island Detection Threshold Setting Using Phasor Measurement Unit Voltage Angle in a Distribution Network," Energies, MDPI, vol. 14(16), pages 1-14, August.
    2. Md Mainul Islam & Mahmood Nagrial & Jamal Rizk & Ali Hellany, 2021. "General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review," Sustainability, MDPI, vol. 13(16), pages 1-45, August.
    3. 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.
    4. Reza Bakhshi-Jafarabadi & Marjan Popov, 2021. "Hybrid Islanding Detection Method of Photovoltaic-Based Microgrid Using Reference Current Disturbance," Energies, MDPI, vol. 14(5), pages 1-15, March.
    5. Marek Wasowski & Tomasz Sikorski & Grzegorz Wisniewski & Pawel Kostyla & Jaroslaw Szymanda & Marcin Habrych & Lukasz Gornicki & Jaroslaw Sokol & Mariusz Jurczyk, 2021. "The Impact of Supply Voltage Waveform Distortion on Non-Intentional Emission in the Frequency Range 2–150 kHz: An Experimental Study with Power-Line Communication and Selected End-User Equipment," Energies, MDPI, vol. 14(3), pages 1-26, February.
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    Cited by:

    1. Sowmya Ramachandradurai & Narayanan Krishnan & Gulshan Sharma & Pitshou N. Bokoro, 2023. "Islanding Detection with Reduced Non-Detection Zones and Restoration by Reconfiguration," Energies, MDPI, vol. 16(7), pages 1-19, March.
    2. Sowmya Ramachandradurai & Narayanan Krishnan & Natarajan Prabaharan, 2022. "Unintentional Passive Islanding Detection and Prevention Method with Reduced Non-Detection Zones," Energies, MDPI, vol. 15(9), pages 1-26, April.
    3. Juan R. Lopez & Jose de Jesus Camacho & Pedro Ponce & Brian MacCleery & Arturo Molina, 2022. "A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network," Energies, MDPI, vol. 15(19), pages 1-25, October.

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