IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4565-d845281.html
   My bibliography  Save this article

A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources

Author

Listed:
  • Peng Cheng

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China)

  • Zhiyu Xu

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China)

  • Ruiye Li

    (College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
    Innovation Laboratory for Science and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China)

  • Chao Shi

    (Comac Beijing Civil Aircraft Center, Beijing 102209, China)

Abstract

In order to realize the sustainable development of energy, the combination of new energy power generation technology and the traditional offshore platform has excellent research prospects. The access to new energy sources can provide a powerful supplement to the power grid of the offshore platform, but will also create new challenges for the planning, operation, and control of the power grid of the platform; hence, it is very important to optimize the reactive power of the offshore platform with new study, a mathematical model was first built for the reactive power optimization of offshore platform power systems with new energy sources, and the Taguchi method was then used to optimize the parameters and population of particle swarm optimization, thereby addressing a defect in particle swarm optimization, namely, that it can easily fall into local optimal solutions. Finally, the algorithm proposed in this paper was applied to solve the reactive power optimization problem of the offshore platform power system with new energy sources. The experimental results show that the proposed algorithm has stronger optimization ability, reduces the system active power loss to the greatest extent, and improves the voltage quality. These results provide theoretical support for the practical application and optimization of the deep-water semi-submersible production platform integrated with new energy sources.

Suggested Citation

  • Peng Cheng & Zhiyu Xu & Ruiye Li & Chao Shi, 2022. "A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources," Energies, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4565-:d:845281
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4565/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4565/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zahir Sahli & Abdellatif Hamouda & Abdelghani Bekrar & Damien Trentesaux, 2018. "Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †," Energies, MDPI, vol. 11(8), pages 1-21, August.
    2. Carolina G. Marcelino & João V. C. Avancini & Carla A. D. M. Delgado & Elizabeth F. Wanner & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    3. Bayat, A. & Bagheri, A., 2019. "Optimal active and reactive power allocation in distribution networks using a novel heuristic approach," Applied Energy, Elsevier, vol. 233, pages 71-85.
    4. Mini Vishnu & Sunil Kumar T. K., 2020. "An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization," Energies, MDPI, vol. 13(11), pages 1-21, June.
    5. Fengli Jiang & Yichi Zhang & Yu Zhang & Xiaomeng Liu & Chunling Chen, 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization," Energies, MDPI, vol. 12(9), pages 1-14, May.
    Full references (including those not matched with items on IDEAS)

    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. Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
    2. Lenin Kanagasabai, 2022. "Real power loss dwindling and voltage reliability enrichment by gradient based optimization algorithm," 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. 13(5), pages 2727-2742, October.
    3. Lenin Kanagasabai, 2023. "Real power loss reduction by extreme learning machine based Panthera leo, chaotic based Jungle search and Quantum based Chipmunk search optimization algorithms," 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. 14(1), pages 55-78, March.
    4. Lenin Kanagasabai, 2022. "Mathematics based calculation and stemonitis inspired optimization algorithms for loss reduction and power solidity augmentation," 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. 13(5), pages 2710-2726, October.
    5. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac, 2021. "Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm," Energies, MDPI, vol. 14(5), pages 1-20, February.
    6. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2019. "Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms," Energies, MDPI, vol. 12(22), pages 1-36, November.
    7. Lenin Kanagasabai, 2022. "Buoyancy based optimization algorithm for real power loss diminution," 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. 13(5), pages 2442-2457, October.
    8. 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.
    9. Chandrasekaran Venkatesan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems," Sustainability, MDPI, vol. 13(6), pages 1-34, March.
    10. Samson Ademola Adegoke & Yanxia Sun, 2023. "Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight," Energies, MDPI, vol. 16(3), pages 1-14, January.
    11. Samson Ademola Adegoke & Yanxia Sun & Zenghui Wang, 2023. "Minimization of Active Power Loss Using Enhanced Particle Swarm Optimization," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    12. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2023. "Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks," Energies, MDPI, vol. 17(1), pages 1-24, December.
    13. Devabalaji Kaliaperumal Rukmani & Yuvaraj Thangaraj & Umashankar Subramaniam & Sitharthan Ramachandran & Rajvikram Madurai Elavarasan & Narottam Das & Luis Baringo & Mohamed Imran Abdul Rasheed, 2020. "A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks Using Bio-Inspired Cuckoo Search Algorithm," Energies, MDPI, vol. 13(18), pages 1-21, September.
    14. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    15. Pereira, Luan D.L. & Yahyaoui, Imene & Fiorotti, Rodrigo & de Menezes, Luíza S. & Fardin, Jussara F. & Rocha, Helder R.O. & Tadeo, Fernando, 2022. "Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations," Applied Energy, Elsevier, vol. 307(C).
    16. Lenin Kanagasabai, 2022. "Real power loss reduction by quantum based Ptilonorhynchus violaceus optimization and Haliastur Indus algorithms," 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. 13(4), pages 1913-1931, August.
    17. Syed Ali Abbas Kazmi & Usama Ameer Khan & Hafiz Waleed Ahmad & Sajid Ali & Dong Ryeol Shin, 2020. "A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth," Energies, MDPI, vol. 13(6), pages 1-71, March.
    18. Hao He & Jia Li & Weizhe Zhao & Boyang Li & Yalong Li, 2022. "Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm," Energies, MDPI, vol. 15(10), pages 1-12, May.
    19. Ayat Ali Saleh & Tomonobu Senjyu & Salem Alkhalaf & Majed A. Alotaibi & Ashraf M. Hemeida, 2020. "Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models," Energies, MDPI, vol. 13(21), pages 1-24, November.
    20. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, 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:gam:jeners:v:15:y:2022:i:13:p:4565-:d:845281. 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.