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Real power loss reduction by enhanced Apple Maggot optimization algorithm

Author

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  • Lenin Kanagasabai

    (Prasad V.Potluri Siddhartha Institute of Technology)

Abstract

In this paper Enhanced Apple Maggot Optimization (EAMO) Algorithm is used to solve the power loss lessening problem. Core objectives of the paper are loss lessening, Power constancy augmentation and power divergence curtailing. In Proposed approach normal events of Apple Maggot are used to design the Apple Maggot Optimization algorithm. Outstanding vision and sharing the information, makes the Apple Maggot to discover the foodstuff devoid of complicatedness. Each Apple Maggot upholds a lane from its location and concentration will be in the direction of foodstuff. Dissimilar categories of aroma are compared, and the largest foodstuff area will be selected. Apple Maggot, utilize a capricious direction and expanse to create novel entity position. This capricious method does not create any utilization of the information engendered and it greatly condenses Apple Maggot multiplicity once they not converse with one another. This lessening in diversity will create the procedure vulnerable to local optima. Also the accumulation of solution near to the local optima should be avoided. In the Enhanced Apple Maggot Optimization (EAMO) Algorithm, Bat sonar technique has been amalgamated to augment the diversity, in order to reach the global optimal solution. Authenticity of the Enhanced Apple Maggot Optimization (EAMO) Algorithm is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Proportion of actual power loss lessening is amplified.

Suggested Citation

  • Lenin Kanagasabai, 2021. "Real power loss reduction by enhanced Apple Maggot 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. 12(6), pages 1385-1396, December.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:6:d:10.1007_s13198-021-01321-6
    DOI: 10.1007/s13198-021-01321-6
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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.
    2. Lalit Kumar & Sushil Kumar Gupta & Sanjay Kumar, 2022. "A novel brainstorm based optimization method for optimum planning of reactive power with FACTS devices," 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(6), pages 3062-3073, December.

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