IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4263-d835494.html
   My bibliography  Save this article

MTDC Grids: A Metaheuristic Solution for Nonlinear Control

Author

Listed:
  • Muhammad Zain Yousaf

    (School of Electrical Engineering, Guangxi University, Nanning 530600, China)

  • Ali Raza

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54000, Pakistan)

  • Ghulam Abbas

    (Department of Electrical Engineering, University of Lahore, Lahore 54000, Pakistan)

  • Nasim Ullah

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Ahmad Aziz Al-Ahmadi

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Abdul Rehman Yasin

    (Department of Electrical Engineering, University of Lahore, Lahore 54000, Pakistan)

  • Mohsin Jamil

    (Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

Abstract

This scientific paper aims to increase the voltage source converter (VSC) control efficiency in a multi-terminal high voltage direct current (MTDC) network during dynamic operations. In the proposed study, the Mayfly algorithm (MA) is used to modify the control parameters of VSC stations. Traditional strategies that modify VSC control settings using approximate linear models fail to produce optimal results because VSCs are nonlinear characteristics of the MTDC system. Particle swarm optimization (PSO) may produce optimal outcomes, but it is prone to becoming stuck in a local optimum. To modify the proportional-integral (P.I.) control parameters of the VSC station, the Mayfly algorithm, a modified form of PSO, is used. The suggested algorithm’s objective function simultaneously optimizes both the outer and inner control layers. A four-terminal MTDC test system is developed in PSCAD/EMTDC to assess the benefits of the proposed algorithm. For VSCs, a comparison of classical, PSO, and proposed MA-based tuned parameters is carried out. The integral of time multiplied by absolute error (ITAE) criterion is used to compare the performance of classical, PSO, and a proposed algorithm for VSC controller parameters/gains. With an ITAE value of 6.8521 × 10 −6 , the MA-based proposed algorithm computes the optimal values and outperforms its predecessor to adjust the VSCs controller gains. For (i) wind farm power variation, (ii) AC grid load demand variation, and (iii) ultimate permanent VSC disconnection, steady-state and dynamic performances are evaluated. According to the results, the proposed algorithm based MTDC system performs well during transients.

Suggested Citation

  • Muhammad Zain Yousaf & Ali Raza & Ghulam Abbas & Nasim Ullah & Ahmad Aziz Al-Ahmadi & Abdul Rehman Yasin & Mohsin Jamil, 2022. "MTDC Grids: A Metaheuristic Solution for Nonlinear Control," Energies, MDPI, vol. 15(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4263-:d:835494
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4263/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4263/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Syed F Faisal & Abdul R Beig & Sunil Thomas, 2020. "Time Domain Particle Swarm Optimization of PI Controllers for Bidirectional VSC HVDC Light System," Energies, MDPI, vol. 13(4), pages 1-15, February.
    2. Chi, Fang'ai & Xu, Ying, 2022. "Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 307(C).
    3. Icaza, Daniel & Borge-Diez, David & Galindo, Santiago Pulla, 2022. "Analysis and proposal of energy planning and renewable energy plans in South America: Case study of Ecuador," Renewable Energy, Elsevier, vol. 182(C), pages 314-342.
    4. Muhammad Ahmad Khan & Xiaocong Li & Muhammad Zain Yousaf & Ali Mustafa & Mingshuo Wei, 2021. "Metaheuristic Based Solution for the Non‐Linear Controller of the Multiterminal High‐Voltage Direct Current Networks," Energies, MDPI, vol. 14(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rodolfo Araneo & Salvatore Celozzi & Stefano Lauria & Erika Stracqualursi & Gianfranco Di Lorenzo & Marco Graziani, 2022. "Recent Trends in Power Systems Modeling and Analysis," Energies, MDPI, vol. 15(23), pages 1-7, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Bryam Paúl Lojano-Riera & Carlos Flores-Vázquez & Juan-Carlos Cobos-Torres & David Vallejo-Ramírez & Daniel Icaza, 2023. "Electromobility with Photovoltaic Generation in an Andean City," Energies, MDPI, vol. 16(15), pages 1-16, July.
    3. Muhammad Ahmad Khan & Xiaocong Li & Muhammad Zain Yousaf & Ali Mustafa & Mingshuo Wei, 2021. "Metaheuristic Based Solution for the Non‐Linear Controller of the Multiterminal High‐Voltage Direct Current Networks," Energies, MDPI, vol. 14(6), pages 1-20, March.
    4. Syed F. Faisal & Abdul R. Beig & Sunil Thomas, 2021. "Real-Time Implementation of Robust Loop-Shaping Controller for a VSC HVDC System," Energies, MDPI, vol. 14(16), pages 1-23, August.
    5. Arévalo, Paul & Cano, Antonio & Jurado, Francisco, 2024. "Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study," Energy, Elsevier, vol. 286(C).
    6. Fangyuan Xie & Yi Wu & Xinqi Wang & Xiling Zhou, 2024. "Optimization Strategies for the Envelope of Student Dormitories in Hot Summer and Cold Winter Regions: Multi-Criteria Assessment Method," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    7. Silvia Ruggiero & Marco Iannantuono & Anastasia Fotopoulou & Dimitra Papadaki & Margarita Niki Assimakopoulos & Rosa Francesca De Masi & Giuseppe Peter Vanoli & Annarita Ferrante, 2022. "Multi-Objective Optimization for Cooling and Interior Natural Lighting in Buildings for Sustainable Renovation," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    8. Hou, Dan & Huang, Jiayu & Wang, Yanyu, 2023. "A comparison of approaches with different constraint handling techniques for energy-efficient building form optimization," Energy, Elsevier, vol. 277(C).
    9. David Borge-Diez, 2022. "Energy Policy, Energy Research, and Energy Politics: An Analytical Review of the Current Situation," Energies, MDPI, vol. 15(23), pages 1-13, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4263-:d:835494. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.