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Rotor angle stability enhancement using DDPG reinforcement agent with Gorilla troops optimized input scaling factors.

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This study introduces a novel Reinforcement Learning (RL)-based Power System Stabilizer (PSS) using Deep Deterministic Policy Gradient (DDPG) for enhanced rotor angle stability. The RL-PSS demonstrates superior transient stability performance compared to conventional methods.

Keywords:
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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Rotor angle stability is crucial for power system reliability.
  • Traditional Power System Stabilizers (PSS) have limitations in adapting to complex grid dynamics.
  • Reinforcement Learning (RL) offers a promising approach for adaptive control.

Purpose of the Study:

  • To develop and evaluate a Reinforcement Learning (RL)-based Power System Stabilizer (PSS) for improving rotor angle stability.
  • To optimize the RL agent's performance using the Gorilla Troops Optimization (GTO) algorithm.
  • To validate the proposed PSS across diverse power system test cases.

Main Methods:

  • A Deep Deterministic Policy Gradient (DDPG) algorithm was employed to train the RL agent for the PSS.
  • Input features included scaled generator accelerating power, its derivative and integral, and real power.
  • The Gorilla Troops Optimization (GTO) algorithm was used to optimize scaling factors for input observations.
  • A discrete reward function focused on generator accelerating power below a threshold.

Main Results:

  • The proposed RL-based PSS demonstrated superior performance in simulations compared to Multiband dw speed-based PSS (MB-PSS), lead-lag dw speed-based PSS (dw-PSS), and lead-lag dPa accelerating power-based PSS (dPa-PSS).
  • The stabilizer exhibited enhanced transient stability capabilities, even under long-duration fault conditions.
  • Simulations were conducted on Single Machine Infinite Bus (SMIB), Kundur's four-machine, and IEEE 39 bus ten-machine systems.

Conclusions:

  • The developed RL-based PSS with DDPG and GTO optimization effectively enhances rotor angle stability.
  • The proposed method offers a robust and adaptive solution for power system stabilization.
  • This approach presents a significant improvement over conventional PSS designs in terms of transient stability.