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

Fayez F M El-Sousy1, Nauman Ali Larik2,3, Wei Lue2

  • 1Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia. f.elsousy@psau.edu.sa.

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|February 20, 2025
PubMed
Summary
This summary is machine-generated.

A new passive islanding detection strategy for microgrids uses a mathematical morphological filter (MMF) and sliding window median filter (SWMBMF). This method achieves 99% accuracy and detects islanding events in 5ms with a negligible non-detection zone (NDZ).

Keywords:
Islanding detectionMathematical morphological filterNon-detection zonePassive methods

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Area of Science:

  • Electrical Engineering
  • Power Systems
  • Renewable Energy Integration

Background:

  • Microgrids with distributed generation (DG) face islanding detection challenges.
  • Traditional methods struggle with detection speed, reliability, and non-detection zones (NDZ).

Purpose of the Study:

  • To present a novel modified passive islanding detection strategy for microgrids.
  • To improve detection speed, reliability, and minimize NDZ in islanding detection.

Main Methods:

  • Utilized a sliding window method-based median filter (SWMBMF) to estimate noisy voltage signals.
  • Employed a mathematical morphological filter (MMF) to compute the voltage residuals index (VRI).
  • Compared VRI with thresholds for islanding detection and distinguishing grid conditions.

Main Results:

  • Achieved 99% accuracy in islanding detection.
  • Successfully identified islanding events within 5ms.
  • Demonstrated negligible non-detection zone (NDZ).
  • Validated robustness through MATLAB/Simulink simulations under various scenarios.

Conclusions:

  • The proposed MMF and SWMBMF strategy effectively addresses microgrid islanding detection challenges.
  • Rapid and reliable islanding detection enhances microgrid stability and operational safety.
  • The method offers a significant improvement over traditional islanding detection techniques.