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End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering

Zhonglin Sun1, Yannis Spyridis1, Thomas Lagkas2,3

  • 10 Infinity Ltd., Imperial Offices, London E6 2JG, UK.

Sensors (Basel, Switzerland)
|March 6, 2021
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Summary

This study introduces a deep learning approach for intentional islanding, a power system stability technique. The method effectively creates stable islands by minimizing power imbalance and adapting to real-time conditions.

Keywords:
deep learninggrap h partitiongraph convolutional networksintentional islandingload-generation balancepower systemspectral clustering

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Power system stability is crucial during emergencies.
  • Cascading failures threaten grid integrity.
  • Intentional islanding is a protective measure involving grid partitioning.

Purpose of the Study:

  • To propose an end-to-end deep learning method for intentional islanding.
  • To minimize load-generation imbalance within formed islands.
  • To enhance islanding stability in complex power systems.

Main Methods:

  • Utilizing a deep learning model for graph partitioning.
  • Examining two loss functions for the partitioning task.
  • Implementing a bus-merging technique to connect isolated buses.
  • Employing real-time system data for dynamic calculations.

Main Results:

  • The deep learning method achieved effective clustering for intentional islanding.
  • Low power imbalance was maintained in the formed islands.
  • Stable islands were successfully created.
  • The dynamic approach improved results for complex systems.

Conclusions:

  • The proposed deep learning method offers an effective solution for intentional islanding.
  • The technique ensures power system stability by creating balanced and reliable islands.
  • The dynamic and adaptive nature of the method is suitable for real-world applications.