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Pelican Optimization Algorithm-Based Proportional–Integral–Derivative Controller for Superior Frequency Regulation in Interconnected Multi-Area Power Generating System

Author

Listed:
  • Abidur Rahman Sagor

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, American International University–Bangladesh, Dhaka 1229, Bangladesh)

  • Md Abu Talha

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, American International University–Bangladesh, Dhaka 1229, Bangladesh)

  • Shameem Ahmad

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, American International University–Bangladesh, Dhaka 1229, Bangladesh)

  • Tofael Ahmed

    (Department of Electrical and Electronic Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh)

  • Mohammad Rafiqul Alam

    (Department of Electrical and Electronic Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh)

  • Md. Rifat Hazari

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, American International University–Bangladesh, Dhaka 1229, Bangladesh)

  • G. M. Shafiullah

    (School of Engineering and Energy, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia)

Abstract

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller’s robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, T p , and T ij ) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively.

Suggested Citation

  • Abidur Rahman Sagor & Md Abu Talha & Shameem Ahmad & Tofael Ahmed & Mohammad Rafiqul Alam & Md. Rifat Hazari & G. M. Shafiullah, 2024. "Pelican Optimization Algorithm-Based Proportional–Integral–Derivative Controller for Superior Frequency Regulation in Interconnected Multi-Area Power Generating System," Energies, MDPI, vol. 17(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3308-:d:1429563
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    References listed on IDEAS

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    1. Daeil Lee & Seoryong Koo & Inseok Jang & Jonghyun Kim, 2022. "Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation," Energies, MDPI, vol. 15(8), pages 1-25, April.
    2. Dhanasekaran Boopathi & Kaliannan Jagatheesan & Baskaran Anand & Sourav Samanta & Nilanjan Dey, 2023. "Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
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