Islanding detection methods for microgrids: A comprehensive review

Muhammed Y. Worku*, Mohamed A. Hassan, Luqman S. Maraaba, Mohammad A. Abido

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations


Microgrids that are integrated with distributed energy resources (DERs) provide many benefits, including high power quality, energy efficiency and low carbon emissions, to the power grid. Microgrids are operated either in grid‐connected or island modes running on different strate-gies. However, one of the major technical issues in a microgrid is unintentional islanding, where failure to trip the microgrid may lead to serious consequences in terms of protection, security, voltage and frequency stability, and safety. Therefore, fast and efficient islanding detection is necessary for reliable microgrid operations. This paper provides an overview of microgrid islanding detection methods, which are classified as local and remote. Various detection methods in each class are stud-ied, and the advantages and disadvantages of each method are discussed based on performance evaluation indices such as non‐detection zone (NDZ), detection time, error detection ratio, power quality and effectiveness in multiple inverter cases. Recent modifications on islanding methods using signal processing techniques and intelligent classifiers are also discussed. Modified passive methods with signal processing and intelligent classifiers are addressing the drawbacks of passive methods and are getting more attention in the recently published works. This comprehensive review of islanding methods will provide power utilities and researchers a reference and guideline to select the best islanding detection method based on their effectiveness and economic feasibility.

Original languageEnglish
Article number3174
Issue number24
StatePublished - 1 Dec 2021

Bibliographical note

Funding Information:
Funding: The authors acknowledge the support provided by the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC‐REPS), Research Institute, King Fahd University of Petroleum and Minerals, through Project #INRE2111.

Publisher Copyright:
© 2021, MDPI. All rights reserved.


  • Islanding detection
  • Local islanding
  • Microgrid
  • Remote islanding
  • Signal processing

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Mathematics (all)
  • Engineering (miscellaneous)


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