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Tuning of Controllers in Power Systems Using a Heuristic-Stochastic Approach

Author

Listed:
  • Humberto Verdejo

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

  • Rodrigo Torres

    (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

A method is proposed to fit parameters of Power System Stabilizer controllers in electromechanical multimachine power systems. The use of the Non-dominated Sorting Genetic Algorithm II heuristic method and Tabu search is considered to be initial search criteria. These methods give an approximation of the values that define the controllers. Then, the stochastic approach was used to evaluate the behavior of the parameters found when considering the system’s response to the presence of random and self-sustained in-time disturbances that affect the response of the system under steady state. The stochastic approach allows the evaluation of the system’s response through the calculation of the cost of energy loss under steady state. The method is applied to two systems: a three-machine nine-busbar system, and the Interconnected System of the Greater North (Sistema Interconectado del Norte Grande) in Chile. For these systems, the proposed methodology effectively optimized the controllers and Tabu search was shown to have a better performance than the Non-dominated Sorting Genetic Algorithm II.

Suggested Citation

  • Humberto Verdejo & Rodrigo Torres & Victor Pino & Wolfgang Kliemann & Cristhian Becker & José Delpiano, 2019. "Tuning of Controllers in Power Systems Using a Heuristic-Stochastic Approach," Energies, MDPI, vol. 12(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2325-:d:240714
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Predrag Marić & Ružica Kljajić & Harold R. Chamorro & Hrvoje Glavaš, 2021. "Power System Stabilizer Tuning Algorithm in a Multimachine System Based on S-Domain and Time Domain System Performance Measures," Energies, MDPI, vol. 14(18), pages 1-30, September.
    3. 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.
    4. Aliyu Sabo & Noor Izzri Abdul Wahab & Mohammad Lutfi Othman & Mai Zurwatul Ahlam Mohd Jaffar & Hakan Acikgoz & Hamzeh Beiranvand, 2020. "Application of Neuro-Fuzzy Controller to Replace SMIB and Interconnected Multi-Machine Power System Stabilizers," Sustainability, MDPI, vol. 12(22), pages 1-42, November.
    5. Michał Izdebski & Robert Małkowski & Piotr Miller, 2022. "New Performance Indices for Power System Stabilizers," Energies, MDPI, vol. 15(24), pages 1-23, December.

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