IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5748-d1208382.html
   My bibliography  Save this article

Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line

Author

Listed:
  • Vincent N. Ogar

    (Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Sajjad Hussain

    (Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
    These authors played the supervisory role.)

  • Kelum A. A. Gamage

    (Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
    These authors played the supervisory role.)

Abstract

Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of the power system. With the increasing integration of renewable energy sources and the growth of complex interconnected grids, efficient and robust LFC strategies are in high demand. In recent years, the combination of particle swarm optimisation (PSO) and proportional-integral-derivative (PID) controllers, known as PSP-PID, has been used as a promising approach to enhance the performance of LFC systems. This article focuses on modelling, simulation, optimisation, advanced control techniques, expert knowledge, and iterative refinement of the power system to help achieve suitable PID settings that provide reliable control of the load frequency in the transmission line. The performance indices of the proposed algorithm are measured by the integral time absolute error (ITAE), which is 0.0005757 with 0.9994 K i , 0.7741 K p , and 0.1850 K d . The model system dynamics are tested by varying the load frequency from 300 MW to 350 MW at a load variation of 0.2. The suggested controller algorithm is relatively reliable and accurate in power system management and protection load frequency control compared to conventional methods. This work can be improved by including more generating stations synchronised into a single network.

Suggested Citation

  • Vincent N. Ogar & Sajjad Hussain & Kelum A. A. Gamage, 2023. "Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line," Energies, MDPI, vol. 16(15), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5748-:d:1208382
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5748/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5748/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deepak Kumar Gupta & Amitkumar V. Jha & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon & Phatiphat Thounthong, 2021. "Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems," Energies, MDPI, vol. 14(6), pages 1-16, 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. Waqar Younis & Muhammad Zubair Yameen & Abu Tayab & Hafiz Ghulam Murtza Qamar & Ehab Ghith & Mehdi Tlija, 2024. "Enhancing Load Frequency Control of Interconnected Power System Using Hybrid PSO-AHA Optimizer," Energies, MDPI, vol. 17(16), pages 1-40, August.
    2. Ashraf K. Abdelaal & Mohamed A. El-Hameed, 2024. "Application of Robust Super Twisting to Load Frequency Control of a Two-Area System Comprising Renewable Energy Resources," Sustainability, MDPI, vol. 16(13), pages 1-15, June.

    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. Bashar Abbas Fadheel & Noor Izzri Abdul Wahab & Ali Jafer Mahdi & Manoharan Premkumar & Mohd Amran Bin Mohd Radzi & Azura Binti Che Soh & Veerapandiyan Veerasamy & Andrew Xavier Raj Irudayaraj, 2023. "A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System," Energies, MDPI, vol. 16(3), pages 1-28, January.
    2. Ch. Naga Sai Kalyan & B. Srikanth Goud & Ch. Rami Reddy & Haitham S. Ramadan & Mohit Bajaj & Ziad M. Ali, 2021. "Water Cycle Algorithm Optimized Type II Fuzzy Controller for Load Frequency Control of a Multi-Area, Multi-Fuel System with Communication Time Delays," Energies, MDPI, vol. 14(17), pages 1-19, August.
    3. Ajay Kumar & Deepak Kumar Gupta & Sriparna Roy Ghatak & Bhargav Appasani & Nicu Bizon & Phatiphat Thounthong, 2022. "A Novel Improved GSA-BPSO Driven PID Controller for Load Frequency Control of Multi-Source Deregulated Power System," Mathematics, MDPI, vol. 10(18), pages 1-41, September.
    4. Deepak Kumar Gupta & Ankit Kumar Soni & Amitkumar V. Jha & Sunil Kumar Mishra & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon & Phatiphat Thounthong, 2021. "Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System," Mathematics, MDPI, vol. 9(7), pages 1-15, March.
    5. Balvinder Singh & Adam Slowik & Shree Krishna Bishnoi, 2022. "A Dual-Stage Controller for Frequency Regulation in a Two-Area Realistic Diverse Hybrid Power System Using Bull–Lion Optimization," Energies, MDPI, vol. 15(21), pages 1-24, October.
    6. Hiramani Shukla & Srete Nikolovski & More Raju & Ankur Singh Rana & Pawan Kumar, 2022. "A Particle Swarm Optimization Technique Tuned TID Controller for Frequency and Voltage Regulation with Penetration of Electric Vehicles and Distributed Generations," Energies, MDPI, vol. 15(21), pages 1-32, November.
    7. Yicong Wang & Chang Liu & Ji Han & Haoyu Tan & Fangchao Ke & Dongyin Zhang & Cong Wei & Shihong Miao, 2022. "A Distributed Frequency Regulation Method for Multi-Area Power System Considering Optimization of Communication Structure," Energies, MDPI, vol. 15(18), pages 1-18, September.

    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:16:y:2023:i:15:p:5748-:d:1208382. 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.