IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v23y2023i3d10.1007_s12351-023-00787-5.html
   My bibliography  Save this article

Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

Author

Listed:
  • Jinzhong Zhang

    (West Anhui University)

  • Tan Zhang

    (West Anhui University)

  • Gang Zhang

    (West Anhui University)

  • Min Kong

    (West Anhui University)

Abstract

This paper proposes an enhanced whale optimization algorithm based on a ranking-based mutation operator (EWOA) to stabilize the PID controller’s parameters in the AVR system, and the intention is to arrive at the ideal objective function value by modifying the control parameters. The whale optimization algorithm is based on the humpback whale’s bubble-net attacking behavior, which imitates shrinkage encircling prey, bubble-net attacking prey and random searching for prey to address the complicated optimization issue. The ranking-based mutation operator can maximize the selection probability, screen out the best search individual, eliminate premature convergence, promote the convergence speed and elevate the exploitation ability. The EWOA not only has substantial robustness and stability to strengthen the optimization efficiency and recognize the optimal solution but also combines exploration with exploitation to expand the convergence rate and calculation precision. The EWOA is contrasted with other algorithms to validate the practicability and usefulness. The experimental results demonstrate that the EWOA has a quicker convergence rate, higher computation precision, shorter execution time and greater stability, which is a remarkable and practical method for addressing the parameter optimization of the PID controller in the AVR system.

Suggested Citation

  • Jinzhong Zhang & Tan Zhang & Gang Zhang & Min Kong, 2023. "Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system," Operational Research, Springer, vol. 23(3), pages 1-26, September.
    • Handle: RePEc:spr:operea:v:23:y:2023:i:3:d:10.1007_s12351-023-00787-5
    DOI: 10.1007/s12351-023-00787-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-023-00787-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-023-00787-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Farid Momayezi & S. Kamal Chaharsooghi & Mohammad Mehdi Sepehri & Ali Husseinzadeh Kashan, 2021. "The capacitated modular single-allocation hub location problem with possibilities of hubs disruptions: modeling and a solution algorithm," Operational Research, Springer, vol. 21(1), pages 139-166, March.
    2. Adil Baykasoğlu & Fehmi Burcin Ozsoydan & M. Emre Senol, 2020. "Weighted superposition attraction algorithm for binary optimization problems," Operational Research, Springer, vol. 20(4), pages 2555-2581, December.
    3. Hualong Du & Pengfei Liu & Qiuyu Cui & Xin Ma & He Wang & Sun Young Cho, 2022. "PID Controller Parameter Optimized by Reformative Artificial Bee Colony Algorithm," Journal of Mathematics, Hindawi, vol. 2022, pages 1-16, February.
    4. Yongquan Zhou & Jinzhong Zhang & Xiao Yang & Ying Ling, 2020. "Optimal reactive power dispatch using water wave optimization algorithm," Operational Research, Springer, vol. 20(4), pages 2537-2553, December.
    5. Umesh Balande & Deepti Shrimankar, 2020. "An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems," Operational Research, Springer, vol. 20(2), pages 985-1010, June.
    6. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    7. Mouhamadou A. M. T. Baldé & Serigne Gueye & Babacar M. Ndiaye, 2021. "A greedy evolutionary hybridization algorithm for the optimal network and quadratic assignment problem," Operational Research, Springer, vol. 21(3), pages 1663-1690, September.
    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. Qingbiao Guo & Boqing Qiao & Yingming Yang & Junting Guo, 2024. "Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm," Energies, MDPI, vol. 17(5), pages 1-16, February.

    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. José García & José Lemus-Romani & Francisco Altimiras & Broderick Crawford & Ricardo Soto & Marcelo Becerra-Rozas & Paola Moraga & Alex Paz Becerra & Alvaro Peña Fritz & Jose-Miguel Rubio & Gino Astor, 2021. "A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    2. Abdelhakim Idir & Laurent Canale & Yassine Bensafia & Khatir Khettab, 2022. "Design and Robust Performance Analysis of Low-Order Approximation of Fractional PID Controller Based on an IABC Algorithm for an Automatic Voltage Regulator System," Energies, MDPI, vol. 15(23), pages 1-20, November.
    3. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.
    4. Deng, Huaijun & Liu, Linna & Fang, Jianyin & Qu, Boyang & Huang, Quanzhen, 2023. "A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 205(C), pages 794-817.
    5. Zhang, Jinzhong & Zhang, Gang & Kong, Min & Zhang, Tan & Wang, Duansong & Chen, Rui, 2023. "CWOA: A novel complex-valued encoding whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 151-188.
    6. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
    7. Li, Maodong & Xu, Guanghui & Lai, Qiang & Chen, Jie, 2022. "A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 71-99.
    8. Rahmati, Reza & Neghabi, Hossein & Bashiri, Mahdi & Salari, Majid, 2023. "Stochastic regional-based profit-maximizing hub location problem: A sustainable overview," Omega, Elsevier, vol. 121(C).
    9. Theogan Logan Pillay & Akshay Kumar Saha, 2024. "A Review of Metaheuristic Optimization Techniques for Effective Energy Conservation in Buildings," Energies, MDPI, vol. 17(7), pages 1-37, March.
    10. Hu, Gang & Du, Bo & Li, Huinan & Wang, Xupeng, 2022. "Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 428-467.
    11. Kang, Helei & Liu, Renyun & Yao, Yifei & Yu, Fanhua, 2023. "Improved Harris hawks optimization for non-convex function optimization and design optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 619-639.
    12. He, Bohao & Jia, Biying & Zhao, Yanghe & Wang, Xu & Wei, Mao & Dietzel, Ranae, 2022. "Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 267(C).
    13. Liu, Jianxun & Shi, Jinfei & Hao, Fei & Dai, Min, 2022. "A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 23-48.
    14. Mohammed Hamouda Ali & Ahmed Mohammed Attiya Soliman & Mohamed Abdeen & Tarek Kandil & Almoataz Y. Abdelaziz & Adel El-Shahat, 2023. "A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources," Energies, MDPI, vol. 16(4), pages 1-39, February.
    15. Mahmoud Hemeida & Tomonobu Senjyu & Salem Alkhalaf & Asmaa Fawzy & Mahrous Ahmed & Dina Osheba, 2022. "Reactive Power Management Based Hybrid GAEO," Sustainability, MDPI, vol. 14(11), pages 1-17, June.

    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:spr:operea:v:23:y:2023:i:3:d:10.1007_s12351-023-00787-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.