Evidence map›Paper›PMID 39979371›Full record

ArticleScientific reports2025

Robust modified passive islanding detection for microgrids using mathematical morphology based dual algorithm.

Fayez F M El-Sousy, Nauman Ali Larik, Wei Lue, Sulaiman Z Almutairi, Mohammed H Alqahtani, Saleh Mobayen

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Fayez F M El-SousyDepartment of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia. f.elsousy@psau.edu.sa.
Nauman Ali LarikChang Yuan Contron Power Safety Technology Company Ltd., Zhuhai, 519085, People's Republic of China.
Wei LueChang Yuan Contron Power Safety Technology Company Ltd., Zhuhai, 519085, People's Republic of China.
Sulaiman Z AlmutairiDepartment of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia.
Mohammed H AlqahtaniDepartment of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia.
Saleh MobayenGraduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 640301, Taiwan.

Funding

Ministry of Education in Saudi Arabia IF2/PSAU/2022/01/23085
6 · The paper itself

Abstract

The integration of distributed generation (DG) in microgrids has brought the challenge of islanding detection, where a portion of the grid operates independently due to disconnection from the main utility. Traditional islanding detection methods often struggle to balance detection speed, reliability, and non-detection zones (NDZ). This paper presents a novel modified passive islanding detection strategy based on a mathematical morphological filter (MMF) with a sliding window method-based median filter (SWMBMF), designed to address these challenges in microgrid systems. The measured noisy voltage signal at point of common coupling (PCC)/DGs-terminal is initially estimated via SWMBMF by continuously monitoring the signals with minimal latency. Then, the MMF is utilized to process the estimated voltage to compute the voltage residuals index (VRI). Moreover, the VRI are compared with pre-specified threshold setting to detect islanding conditions, VRI also effectively distinguishes between normal and islanding conditions. Comprehensive MATLAB/Simulink 2023b simulations demonstrate the robustness of the proposed strategy under various islanding scenarios and grid disturbances, proving its effectiveness in ensuring the stability and reliability of microgrid operations. The proposed method demonstrates an impressive accuracy of 99%, successfully identifying islanding events within 5 ms. This rapid detection enhances grid reliability and minimizes risks associated with delayed islanding detection. The proposed scheme has negligible non detection zone (NDZ).

Indexed as

Islanding detectionMathematical morphological filterNon-detection zonePassive methods

Identifiers

PMID39979371
PMCPMC11842859

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.