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A passive islanding detection method using deep neural bidirectional LSTM-CNN.

Hakan Ozturk1,2, Secil Varbak Nese3

  • 1Department of Electrical and Electronics Engineering, Institute of Pure and Applied Sciences, Marmara University, Istanbul, 34722, Turkey.

Scientific Reports
|June 3, 2026
PubMed
Summary
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A new method using a One-Dimensional Convolutional Neural Network-Bidirectional Long Short-Term Memory (1D CNN-BiLSTM) effectively detects unintentional islanding in microgrids. This advanced technique ensures rapid and reliable islanding detection, enhancing power system safety and stability.

Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Power Systems

Background:

  • Microgrids offer benefits like reduced power loss and improved voltage profiles.
  • Unintentional islanding in connected microgrids poses risks of equipment damage and safety hazards.
  • IEEE Std 1547-2018 mandates islanding detection and control within 2 seconds.

Purpose of the Study:

  • To introduce a novel method for rapid and reliable unintentional islanding detection in microgrids.
  • To address the critical need for timely islanding detection to ensure grid safety and stability.
  • To develop an advanced technique that overcomes limitations of existing islanding detection methods.

Main Methods:

  • A new One-Dimensional Convolutional Neural Network-Bidirectional Long Short-Term Memory (1D CNN-BiLSTM) model was developed for islanding detection.
Keywords:
Deep neural networksDistributed generationIslanding detectionMicrogrid

Related Experiment Videos

  • Power system busbars sensitive to islanding were identified through control and observation.
  • Optimally selected current and voltage time series data from critical busbars were processed by the neural network.

    • Main Results:

    • The proposed 1D CNN-BiLSTM method achieved an islanding detection time of just 10 milliseconds.
    • The Non-Detection Zone (NDZ) rate was significantly reduced to 0.02%.
    • Simulation results on the IEC 61850-7-420 test system validated the method's performance.

    Conclusions:

    • The 1D CNN-BiLSTM method offers a substantial improvement in both detection speed and reliability for unintentional islanding.
    • This technique significantly outperforms existing methods in critical islanding detection scenarios.
    • The study highlights the potential of AI-driven approaches for enhancing microgrid operational safety.