IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i1d10.1007_s13198-021-01295-5.html
   My bibliography  Save this article

Tangible power loss lessening by hybridized beautiful demoiselle-enriched particle swarm and pyramid optimization algorithms

Author

Listed:
  • Lenin Kanagasabai

    (Prasad V. Potluri Siddhartha Institute of Technology)

Abstract

In this paper Hybridization of Beautiful Demoiselle algorithm with Enriched particle swarm optimization (HBDPSO) algorithm and Pyramid Optimization (PO) Algorithm is applied to solve the Real power loss reduction problem. Key objectives of the paper are Voltage stability enhancement, minimization of Voltage deviation and real power loss reduction. Proposed HBDPSO and PO has been applied to solve both in single and multiobjective mode. Beautiful Demoiselle algorithm is modelled based on the standing and vibrant behaviour of Beautiful Demoiselle. In vibrant swarm the Beautiful Demoiselle will fly in specific direction which is analogous to Exploitation phase. Split-up, Connotation, sticking, Enticement to diet and Commotion from competitor are used to mimic the behaviour of Beautiful Demoiselle algorithm in both standing and vibrant swarms. Particle swarm optimization (PSO) algorithm is based on the behaviour of swarm and in the enriched PSO version “s” shaped transfer function will convert the continuous into discrete functions. In Hybridized (HBDPSO)—Beautiful Demoiselle algorithm (BDA) with its competence will stimulate the varied solutions by creation of standing swarms and the augmented version of the particle swarm optimization exploiting the files with its skill to converge to the exceptional global solution in the exploration space. Then in this paper Pyramid Optimization (PO) Algorithm is applied to solve the Real power loss reduction problem. PO algorithm is planned based on the command arrangement which has been found in the higher educational institution. The communication between various levels of workers also with their direct superior, associates and Personal contribution of the staff members are imitated to formulate the algorithm. The notion of institutional pyramid is individuals in an administrative chart permit the cluster of persons to attain their work in an enhanced way. Communication between the persons in that pyramid is an idiosyncratic notion. The custom of the pyramid data association for charting makes the planned algorithm as a ground-breaking methodology. Pyramid is a tree-shaped data structure and it is a widespread tree. In the condition of minimum layer, the weighty of each parental node is either lesser than or equivalent to the vital of its offspring and in condition of maximum layer, the chief of each parental node is either better than or equivalent to the main of its offspring. The entire tree is measured as the population. By means of considering L (voltage constancy)—index Hybridization of Beautiful Demoiselle algorithm with Enriched particle swarm optimization (HBDPSO) algorithm and Pyramid Optimization (PO) Algorithm are corroborated in IEEE 30 Bus system. Then HBDPSO and PO algorithms are appraised in 30 bus test systems (IEEE) deprived of voltage constancy index. HBDPSO and PO algorithms are abridged the power loss proficiently with augmentation in voltage constancy and minimization of voltage deviance. Percentage of power loss reduction has been increased.

Suggested Citation

  • Lenin Kanagasabai, 2022. "Tangible power loss lessening by hybridized beautiful demoiselle-enriched particle swarm and pyramid 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. 13(1), pages 450-468, February.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01295-5
    DOI: 10.1007/s13198-021-01295-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01295-5
    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/s13198-021-01295-5?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. 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.
    2. Li, Jian & Wang, Ni & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal reactive power dispatch of permanent magnet synchronous generator-based wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 145(C), pages 1-12.
    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. 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.
    2. 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.
    3. Wang, Ni & Li, Jian & Yu, Xiang & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal active and reactive power cooperative dispatch strategy of wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 148(C), pages 113-123.
    4. 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.
    5. 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.
    6. Lenin Kanagasabai, 2023. "Real power loss reduction by Toxotes kimberleyensis, Opposition based Chaotic Septentrion Red Snapper and Charidotella based 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(5), pages 1621-1638, October.
    7. Paweł Ocłoń & Maciej Ławryńczuk & Marek Czamara, 2021. "A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation," Energies, MDPI, vol. 14(16), pages 1-15, August.
    8. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    9. Robert Małkowski & Michał Izdebski & Piotr Miller, 2020. "Adaptive Algorithm of a Tap-Changer Controller of the Power Transformer Supplying the Radial Network Reducing the Risk of Voltage Collapse," Energies, MDPI, vol. 13(20), pages 1-25, October.
    10. 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.
    11. 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.
    12. 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.
    13. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    14. Liao, Hao & Hu, Weihao & Wu, Xiawei & Wang, Ni & Liu, Zhou & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Active power dispatch optimization for offshore wind farms considering fatigue distribution," Renewable Energy, Elsevier, vol. 151(C), pages 1173-1185.
    15. 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.
    16. Lenin Kanagasabai, 2022. "Jerusalem artichoke algorithm for power loss reduction and power stability enhancement," 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 1788-1800, August.
    17. Zhang, Guozhou & Hu, Weihao & Cao, Di & Zhou, Dao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Coordinated active and reactive power dynamic dispatch strategy for wind farms to minimize levelized production cost considering system uncertainty: A soft actor-critic approach," Renewable Energy, Elsevier, vol. 218(C).
    18. 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.
    19. Lenin Kanagasabai, 2022. "Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine 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(3), pages 1102-1120, June.
    20. Shojaei, Amir Hossein & Ghadimi, Ali Asghar & Miveh, Mohammad Reza & Gandoman, Foad H. & Ahmadi, Abdollah, 2021. "Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory," Renewable Energy, Elsevier, vol. 163(C), pages 1427-1443.

    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:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01295-5. 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.