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New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm

Author

Listed:
  • Tawfik Guesmi

    (Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia
    Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia)

  • Badr M. Alshammari

    (Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Yasser Almalaq

    (Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Ayoob Alateeq

    (Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

  • Khalid Alqunun

    (Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia)

Abstract

This paper presents a new approach for coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controller. For this purpose, the design problem is considered as an optimization problem whose decision variables are the controllers’ parameters. Due to nonlinearities of large, interconnected power systems, methods capable of handling any nonlinearity of power networks are mostly preferable. In this regard, a nonlinear time domain based objective function is used. Then, the coyote optimization algorithm (COA) is employed for solving this optimization problem. In order to ensure the robustness and performance of the proposed controller (COA-PSS&SVC), the objective function is evaluated for various extreme loading conditions and system configurations. To show the contribution of the coordinated controllers on the improvement of the system stability, PSSs and SVC are optimally designed in individual and coordinated manners. Moreover, the effectiveness of the COA-PSS&SVC is assessed through comparison with other controllers. Nonlinear time domain simulation shows the superiority of the proposed controller and its ability in providing efficient damping of electromechanical oscillations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3131-:d:515846
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    References listed on IDEAS

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    1. Pallavi Choudekar & S. K. Sinha & Anwar Siddiqui, 2017. "Optimal location of SVC for improvement in voltage stability of a power system under normal and contingency condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1312-1318, November.
    2. Sergio Bruno & Giovanni De Carne & Massimo La Scala, 2020. "Distributed FACTS for Power System Transient Stability Control," Energies, MDPI, vol. 13(11), pages 1-16, June.
    3. Conner, Mary M. & Ebinger, Michael R. & Knowlton, Frederick F., 2008. "Evaluating coyote management strategies using a spatially explicit, individual-based, socially structured population model," Ecological Modelling, Elsevier, vol. 219(1), pages 234-247.
    4. Wenping Hu & Jifeng Liang & Yitao Jin & Fuzhang Wu, 2018. "Model of Power System Stabilizer Adapting to Multi-Operating Conditions of Local Power Grid and Parameter Tuning," Sustainability, MDPI, vol. 10(6), pages 1-18, June.
    5. Jouda Arfaoui & Hegazy Rezk & Mujahed Al-Dhaifallah & Mohamed N. Ibrahim & Mami Abdelkader, 2020. "Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System," Energies, MDPI, vol. 13(17), pages 1-17, August.
    6. 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.
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    Citations

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    Cited by:

    1. Adrian Nocoń & Stefan Paszek, 2023. "A Comprehensive Review of Power System Stabilizers," Energies, MDPI, vol. 16(4), pages 1-32, February.
    2. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).
    3. 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.
    4. Ismail Marouani & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Saleh Albadran & Hsan Hadj Abdallah & Salem Rahmani, 2023. "Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods," Sustainability, MDPI, vol. 15(12), pages 1-58, June.

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