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

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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