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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Resiliency Assessment of Power Systems Using Deep Reinforcement Learning.

Mariam Ibrahim1, Ahmad Alsheikh1,2, Ruba Elhafiz1

  • 1Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan.

Computational Intelligence and Neuroscience
|April 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a level-of-resilience (LoR) measure for power systems, using deep reinforcement learning (DRL) to identify vulnerabilities. The double DQN agent proved most effective in assessing power system resiliency against faults.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Power system resilience is critical for reliable operation during abnormal conditions.
  • Assessing resilience aids in developing effective planning and operational strategies.
  • Sequential topology attacks pose a significant threat to power system stability.

Purpose of the Study:

  • To introduce a novel measure, the level-of-resilience (LoR), for quantifying power system resilience.
  • To evaluate the effectiveness of various deep reinforcement learning (DRL) agents in determining LoR.
  • To identify the most efficient DRL agent for power system resiliency assessment.

Main Methods:

  • Development and application of a level-of-resilience (LoR) metric.
  • Utilizing four deep reinforcement learning (DRL) agents: deep Q-network (DQN), double DQN, REINFORCE, and REINFORCE with baseline.
  • Conducting case studies on the IEEE 6-bus test system to simulate sequential topology attacks.

Main Results:

  • The double DQN agent demonstrated the highest success rate in determining the LoR.
  • The double DQN agent exhibited the fastest performance compared to other DRL agents.
  • The proposed LoR measure effectively quantifies power system vulnerability under attack scenarios.

Conclusions:

  • Deep reinforcement learning, particularly the double DQN agent, is a highly efficient tool for power system resiliency evaluation.
  • The LoR measure provides a valuable metric for understanding and enhancing power system robustness.
  • Findings support the adoption of advanced AI techniques for critical infrastructure protection.