International Journal of

Automation and Smart Technology

Lenin Kanagasabai1

 

1Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India 

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ABSTRACT

In this paper Extreme Learning Machine based - Xiphias Optimization Algorithm (ELMXOA) and Gilt-head bream Optimization Algorithm (ELMGHO) applied to solve the problem. Entrant solutions in the proposed procedure are Xiphias and populace in the exploration zone, capriciously created. Gilt-head bream Optimization Algorithm designed by imitating the actions of Gilt-head bream’s supportive stalking physiognomies. In cluster mode, the Gilt-head bream hunt the victim, it is entitled as key cluster (population), and additional cluster is termed as sub – population. Proposed ELM based - Xiphias Optimization Algorithm (ELMXOA) and Gilt-head bream Optimization Algorithm (ELMGHO) corroborated in IEEE 300 and 220 KV systems.


Keywords: Extreme Learning Machine; Xiphias; Gilt-head bream


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REFERENCES

  1. [1] N.Karthik, P.Arul, “Multi-Objective Optimal Reactive Power Dispatch using Levy Interior Search Algorithm”, International Journal on Electrical Engineering and Informatics, 12, 547–570, 2020. https://doi.org/10.15676/ijeei.2020.12.3.8

  2. [2] S.Mouassa, T.Bouktir, “Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem”, COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 35 No. 1, pp. 350–372, 2018. https://doi.org/10.1108/COMPEL-05-2018-0208

  3. [3] Https://icseg.iti.illinois.edu/ieee-300-bus / accessed on 1.02.2023 at 10.11 IST

  4. [4] M. Mouwafi, Adel A. Abou El-Ela, A. El-Sehiemy, K. Al-Zahar, “Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm,” Alexandria Engineering Journal, Volume 61, Issue 2, 1383–1401, 2022. https://doi.org/10.1016/j.aej.2021.06.021

  5. [5] A.AbouEl-Ela, M.Mouwafi, W.Al-Zahar, "Optimal Transmission System Expansion Planning Via Binary Bat Algorithm," 2019 21st International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 2019, pp. 238–243. https://doi.org/10.1109/MEPCON47431.2019.9008022

  6. [6] L.Kanagasabai, “Novel Western Jackdaw Search, Antrostomus Swarm and Indian Ethnic Vedic Teaching – Inspired Optimization Algorithms for Real Power Loss Diminishing and Voltage Consistency Growth,” International Journal of System Assurance Engineering and Management, 13(6), 2895–2919, 2022. https://doi.org/10.1007/s13198-022-01758-3

  7. [7] M. Rasyid Ridho, H. Nuha, "Application of Extreme Learning Machine (ELM) Classification in Detecting Phishing Sites," 2022 5th International Conference of Computer and Informatics Engineering (IC2IE),” Jakarta, Indonesia, 2022, pp. 60-64. https://doi.org/10.1109/IC2IE56416.2022.9970191

  8. [8] S.Fan, F.Li, B.Wang, X.Zhang, P.Yan, "ELM Based Improved Layered Ensemble Architecture for Security Situation Prediction," 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ), Beijing, China, 2022, pp. 630–634. https://doi.org/10.1109/IAEAC54830.2022.9929487

  9. [9] H.Zhu, Z.Lv, R.Di, H.Gao, K.Hao, W.Peng, "Remaining Useful Life Prediction of Lithium Battery Based on SSA-ELM," 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi'an, China, 2022, pp. 307–310. https://doi.org/10.1109/ICCNEA57056.2022.00074

  10. [10] K.Ari, T.Suesut, W.Tangsrirat, G.Schleining, N.Nunak, "Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm" Applied Sciences 13, no. 6: 3812. 2023. https://doi.org/10.3390/app13063812

  11. [11] V.Ananthan, "Fish Species Detection and Tracking Based on Fusion Intensity Entity Transformation using Optical Flow Algorithm," 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022, pp. 1711–1716. https://doi.org/10.1109/ICECAA55415.2022.9936540

  12. [12] A.Raihan, M. Monju, M. MehediHasan, M. Tarek Habib, M.S.Uddin, "CNN Modeling for Recognizing Local Fish," 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2021, pp. 1–5. https://doi.org/10.1109/ICCIT54785.2021.9689898

  13. [13] C.Xin, X.Han, Z.Zhou , Y.Jin, G. Yu, "Visual Power System based on Artificial Fish Swarm Algorithm," 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, 2022, pp. 927–930. https://doi.org/10.1109/ICETCI55101.2022.9832147

  14. [14] J.Wu, Y.Zhou. H.Yu, Y.Zhang, J.Li, "A Novel Fish Counting Method with Adaptive Weighted Multi-Dilated Convolutional Neural Network," 2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS), London, United Kingdom, 2021, pp. 178–183. https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00039


ARTICLE INFORMATION



Accepted: 2023-05-01
Available Online: 2023-05-01


Cite this article:

Kanagasabai, L. (2023) Real Power Loss Reduction by Extreme Learning Machine based - Xiphias and Gilt-head bream Optimization Algorithms. Int. j. autom. smart technol. https://doi.org/10.5875/ausmt.v13i1.2354

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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