IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i3d10.1007_s13198-021-01399-y.html
   My bibliography  Save this article

Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms

Author

Listed:
  • Lenin Kanagasabai

    (Prasad V. Potluri Siddhartha Institute of Technology)

Abstract

In this paper Hybrid Pan troglodytes optimization algorithm—Extreme learning machine based augmented Sine–Cosine algorithm (HPSC) used to solve the power loss lessening problem. Pan troglodytes optimization (PTO) algorithm is designed grounded on the Individual intellect and sexual inducement in constellation mode. Initial solution is presumed to be heightened and acquainted of the position of the objective by the raider, impediment, tracker and navigator. Grounded on the functions of Sine -Cosine a procedure is designed and it titled as sine cosine optimization algorithm (SCA). It stimulates principal whimsical agent solutions and it oscillates on the external or internal in the way of the superlative solution by using numerical model which grounded on the functions of sine and cosine. It has been pragmatic that the orthodox SCA has many drawbacks like trapping in local optimal solution and sluggish convergence. In order to augment the performance, a mutation operative applied to conventional SCA and it called as Augmented SCA (ASCA). The foremost aim of extreme learning machine based augmented sine- cosine optimization algorithm (EASCA) is to diminish the standard of the output weights and to bind the hidden node limitations within a precise range with an intention to improve the convergence performance. In Hybrid Pan troglodytes optimization algorithm—Extreme learning machine based augmented Sine–Cosine algorithm (HPSC) exploration and exploitation are enhanced to reach the optimal solutions. For refining exploitation segment of the Pan troglodytes optimization (PTO) algorithm the sine–cosine functions are amalgamated in the location modernizing equations of the HPSC algorithm. In the exploration realm for recognize the precise and finest global optimum for multifaceted optimization functions have been applied in this improved procedure. Proposed Hybrid Pan troglodytes optimization algorithm—External machine learning based augmented Sine–Cosine algorithm (HPSC) optimization algorithm is appraised in IEEE 30- bus system. Loss lessening achieved, voltage deviancy condensed, and voltage constancy intensified.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01399-y
    DOI: 10.1007/s13198-021-01399-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01399-y
    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/s13198-021-01399-y?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. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    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. Mousumi Banerjee & Vanita Garg & Kusum Deep, 2023. "Solving structural and reliability optimization problems using efficient mutation strategies embedded in sine cosine 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. 14(1), pages 307-327, March.

    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. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    2. 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.
    3. 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.
    4. Ding, Jie & Xu, Yujie & Chen, Haisheng & Sun, Wenwen & Hu, Shan & Sun, Shuang, 2019. "Value and economic estimation model for grid-scale energy storage in monopoly power markets," Applied Energy, Elsevier, vol. 240(C), pages 986-1002.
    5. 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.
    6. 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.
    7. 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.
    8. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    9. 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.
    10. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn, 2021. "Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm," Sustainability, MDPI, vol. 13(13), pages 1-21, July.
    11. 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.
    12. 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.
    13. 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.
    14. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Li, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
    16. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Frede Blaabjerg & Pu Li, 2020. "Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems," Energies, MDPI, vol. 13(7), pages 1-17, April.
    17. 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.
    18. Jaka Marguč & Mitja Truntič & Miran Rodič & Miro Milanovič, 2019. "FPGA Based Real-Time Emulation System for Power Electronics Converters," Energies, MDPI, vol. 12(6), pages 1-23, March.
    19. 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.
    20. 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.

    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:3:d:10.1007_s13198-021-01399-y. 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.