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Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

Author

Listed:
  • Humberto Verdejo

    (Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
    These authors contributed equally to this work.)

  • Victor Pino

    (Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
    These authors contributed equally to this work.)

  • Wolfgang Kliemann

    (Department of Mathematics, Iowa State University, Ames, IA 50011, USA)

  • Cristhian Becker

    (Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
    These authors contributed equally to this work.)

  • José Delpiano

    (School of Engineering and Applied Sciences, Universidad de los Andes, Santiago 7620001, Chile
    Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390212, Chile)

Abstract

The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

Suggested Citation

  • Humberto Verdejo & Victor Pino & Wolfgang Kliemann & Cristhian Becker & José Delpiano, 2020. "Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems," Energies, MDPI, vol. 13(8), pages 1-29, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2093-:d:349077
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    Citations

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    Cited by:

    1. Anurag Gautam & Ibraheem & Gulshan Sharma & Mohammad F. Ahmer & Narayanan Krishnan, 2023. "Methods and Methodologies for Congestion Alleviation in the DPS: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-28, February.
    2. Aliyu Sabo & Bashir Yunus Kolapo & Theophilus Ebuka Odoh & Musa Dyari & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy, 2022. "Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review," Energies, MDPI, vol. 16(1), pages 1-32, December.
    3. Tawfik Guesmi & Badr M. Alshammari & Yasser Almalaq & Ayoob Alateeq & Khalid Alqunun, 2021. "New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    4. Abdul Waheed Khawaja & Nor Azwan Mohamed Kamari & Muhammad Ammirrul Atiqi Mohd Zainuri, 2021. "Design of a Damping Controller Using the SCA Optimization Technique for the Improvement of Small Signal Stability of a Single Machine Connected to an Infinite Bus System," Energies, MDPI, vol. 14(11), pages 1-20, May.
    5. Michał Izdebski & Robert Małkowski & Piotr Miller, 2022. "New Performance Indices for Power System Stabilizers," Energies, MDPI, vol. 15(24), pages 1-23, December.
    6. Zhaojuan Zhang & Wanliang Wang & Gaofeng Pan, 2020. "A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem," Mathematics, MDPI, vol. 8(11), pages 1-21, October.
    7. Jong Ju Kim & June Ho Park, 2021. "A Novel Structure of a Power System Stabilizer for Microgrids," Energies, MDPI, vol. 14(4), pages 1-33, February.

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