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Related Experiment Video

Updated: Jun 6, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
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Enhanced power system stabilizer tuning using marine predator algorithm with comparative analysis and real time

Intissar Hattabi1, Aissa Kheldoun2, Rafik Bradai3

  • 1SET Laboratory, Electrical and Control Department, Faculty of Technology, Blida 1 University, 09000, Blida, Algeria.

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Summary

The Marine Predator Algorithm (MPA) effectively tunes power system stabilizers (PSS) to reduce low-frequency oscillations. MPA-tuned PSS significantly outperforms other methods, enhancing power system stability.

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Computational Intelligence

Background:

  • Low-frequency oscillations (LFOs) are a significant concern in modern power systems, potentially leading to instability.
  • Power System Stabilizers (PSS) are crucial control devices for mitigating these oscillations.
  • Metaheuristic algorithms offer advanced optimization capabilities for tuning PSS parameters.

Purpose of the Study:

  • To implement and evaluate the Marine Predator Algorithm (MPA) for optimizing Power System Stabilizer (PSS) parameters.
  • To enhance the damping of low-frequency oscillations in various power system test models.
  • To compare the performance of MPA with other leading metaheuristic algorithms.

Main Methods:

  • The Marine Predator Algorithm (MPA) was employed for tuning PSS parameters.
  • Simulations were conducted on Single Machine Infinite Bus (SMIB), WSCC, and New England 10-machine 39-bus power systems.
  • Performance was evaluated against Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and others using various fitness functions.
  • Real-time digital simulation (CU-SLRT Std) and Hardware-in-the-Loop (HIL) implementation validated the results.

Main Results:

  • MPA-optimized PSS demonstrated superior performance in damping low-frequency oscillations compared to other algorithms.
  • Improvements reached up to 98.62% over PSO, 71.79% over WOA, and 78.04% over African Vulture Optimization Algorithm (AVOA).
  • Validation on multiple test systems and through HIL confirmed the robustness and effectiveness of the MPA approach.

Conclusions:

  • The Marine Predator Algorithm (MPA) is a highly effective method for tuning PSS parameters to improve power system stability.
  • MPA offers significant advantages in damping low-frequency oscillations, outperforming established optimization techniques.
  • The study validates MPA's practical applicability through real-time and HIL implementations.