Skip to main content
SpringerLink
Log in

Active Power Loss Diminution by Chaotic-Based Adaptive Butterfly Mating Optimization Algorithm

  • Conference paper
  • First Online:
Advances in Communication and Computational Technology (ICACCT 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 668))

  • 1753 Accesses

Abstract

Real power loss minimization is the key objective of this work, and it has been attained by applying chaotic-based adaptive butterfly mating optimization algorithm (CABM). Butterfly mating limitations and scattering in the exploration area are articulated in the CABM algorithm. Butterfly positioned near to the border may get puzzled regarding the direction to the border since UV augments amid of an increase in distance. Through indistinguishable unchanging step, acceleration of butterflies is restricted in iterations. Chaotic-based adaptive butterfly mating optimization (CABM) algorithm’s validity is verified by testing in IEEE 57 bus test system. Projected CABM algorithm reduced the power loss effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lee K (1984) Fuel-cost minimisation for both real and reactive-power dispatches. Proc Gener Transm Distrib Conf 131(3):85–93

    Article  Google Scholar 

  2. Deeb N (1998) An efficient technique for reactive power dispatch using a revised linear programming approach. Electr Power Syst Res 15(2):121–134

    Google Scholar 

  3. Bjelogrlic M (1990) Application of Newton’s optimal power flow in voltage/reactive power control. IEEE Trans Power Syst 5(4):1447–1454

    Article  Google Scholar 

  4. Granville S (1994) Optimal reactive dispatch through interior point methods. IEEE Trans Power Syst 9(1):136–146

    Article  Google Scholar 

  5. Grudinin N (1998) Reactive power optimization using successive quadratic programming method. IEEE Trans Power Syst 13(4):1219–1225

    Article  Google Scholar 

  6. Mei RNS (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput 59:210–222

    Article  Google Scholar 

  7. Chen G (2017) Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints. Appl Soft Comput 50:58–70

    Article  Google Scholar 

  8. Naderi E (2017) Novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Appl Soft Comput 53:441–456

    Article  Google Scholar 

  9. Heidari A (2017) Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl Soft Comput 57:657–671

    Article  Google Scholar 

  10. Morgan M (2016) Benchmark studies on optimal reactive power dispatch (ORPD) based multi-objective evolutionary programming (MOEP) using mutation based on adaptive mutation adapter (AMO) and polynomial mutation operator (PMO). J Electr Syst 12–1

    Google Scholar 

  11. Mei RNS (2016) Ant lion optimizer for optimal reactive power dispatch solution. J Electr Syst 68–74

    Google Scholar 

  12. Anbarasan,: Optimal reactive power dispatch problem solved by symbiotic organism search algorithm. Innov Power Adv Comput Technol 1–8

    Google Scholar 

  13. Gagliano A (2017) Analysis of the performances of electric energy storage in residential applications. Int J Heat Technol 35(1):S41–S48

    Google Scholar 

  14. Caldera M (2018) Survey-based analysis of the electrical energy demand in Italian households. Math Model Eng Probl 5(3):217–224

    Google Scholar 

  15. Basu M (2016) Quasi-oppositional differential evolution for optimal reactive power dispatch. Electr Power Energy Syst 78:29–40

    Google Scholar 

  16. Wu H-N (2017) A novel binary butterfly mating optimization algorithm with sub array strategy for thinning of large antenna array. Prog Electromagn Res M 60:101–110

    Article  Google Scholar 

  17. IEEE The IEEE-test systems http://www.ee.washington.edu/trsearch/pstca/.2019/01/21

  18. Hussain AN (2018) Modified particle swarm optimization for solution of reactive power dispatch. Res J Appl Sci Eng Technol 15(8):316–327

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of EEE, Prasad V. Potluri, Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India

    Kanagasabai Lenin

Authors
  1. Kanagasabai Lenin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kanagasabai Lenin .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Maryland Eastern Shore, Princess Anne, MD, USA

    Gurdeep Singh Hura

  2. Department of Master of Computer Applications, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, India

    Ashutosh Kumar Singh

  3. Department of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia

    Lau Siong Hoe

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lenin, K. (2021). Active Power Loss Diminution by Chaotic-Based Adaptive Butterfly Mating Optimization Algorithm. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5341-7_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5340-0

  • Online ISBN: 978-981-15-5341-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation