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An Extensive Review and Analysis of Islanding Detection Techniques in DG Systems Connected to Power Grids

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  • Mohammad Abu Sarhan

    (Department of Power Electronics and Energy Control Systems, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

Nowadays, the integration of distributed generators with the main utility grid is highly increasing due to the benefits which can be obtained, such as increasing the system efficiency and reliability. Apart from that, many technical and safety issues appear in the system due to this integration. One of these issues is the islanding condition, which has to be detected effectively and quickly before having any detrimental effects on the protection, stability, and security of the system. This study provides a detailed overview of several islanding detection approaches, which are divided into traditional methods, including local and remote methods, and modern methods, including methods based on signal processing and computational intelligence. Moreover, a comparison between each method based on various criteria, such as non-detected zone, quality factor, response time, implementation cost, degrading power quality, reliability, suitability for the type of distributed generators, suitability for multi-distributed generators system, and sensitivity to cyber-attacks, is carried out. Therefore, this review will offer a solid background in order to help researchers interested in this field distinguish between islanding detection methods and their relative advantages and disadvantages, as well as to be able to choose the most suitable islanding detection method among the others to be implemented in the network.

Suggested Citation

  • Mohammad Abu Sarhan, 2023. "An Extensive Review and Analysis of Islanding Detection Techniques in DG Systems Connected to Power Grids," Energies, MDPI, vol. 16(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3678-:d:1132392
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    References listed on IDEAS

    as
    1. Trujillo, C.L. & Velasco, D. & Figueres, E. & Garcerá, G., 2010. "Analysis of active islanding detection methods for grid-connected microinverters for renewable energy processing," Applied Energy, Elsevier, vol. 87(11), pages 3591-3605, November.
    2. 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.
    3. 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.
    4. 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.
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    Cited by:

    1. Sareddy Venkata Rami Reddy & T. R. Premila & Ch. Rami Reddy & Mohammed A. Alharbi & Basem Alamri, 2023. "Passive Island Detection Method Based on Sequence Impedance Component and Load-Shedding Implementation," Energies, MDPI, vol. 16(16), pages 1-14, August.
    2. Hossein Amini & Ali Mehrizi-Sani & Reza Noroozian, 2024. "Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment," Energies, MDPI, vol. 17(17), pages 1-20, September.
    3. Eduardo Marcelo Seguin Batadi & Maximiliano Martínez & Marcelo Gustavo Molina, 2024. "Bayesian Entropy Methodology: A Novel Approach to Setting Anti-Islanding Protections with Enhanced Stability and Sensibility," Energies, MDPI, vol. 17(3), pages 1-26, January.

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