banner
Home > Open Journal Systems

Novel scientific design of hybrid opposition based—Chaotic little golden-mantled flying fox, White-winged chough search optimization algorithm for real power loss reduction and voltage stability expansion

Lenin Kanagasabai

Abstract


In this paper hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is applied to solve the loss dwindling problem. Key objective of the paper is real power loss reduction, voltage deviation minimization and voltage stability expansion. Proposed little golden-mantled flying fox algorithm is designed based on the deeds of the little golden-mantled flying fox. Maximum classes have single progenies at a period afterwards of prenatal period. This little procreative production means that when populace forfeiture their figures are deliberate to ricochet. In White-winged chough search optimization algorithm magnifying the encumbrance element in a definite assortment will pointedly enlarge the exploration region. In a coiled exploration, the position of any White-winged chough can differ in numerous scopes to cover the exploration region, predominantly in the projected problem. Hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is accomplished by integrating the actions of little golden-mantled flying fox and White-winged chough. Through the hybridization of both algorithms exploration and exploitation has been balanced throughout the procedure. Proposed hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is corroborated in IEEE 30 and 57 systems. From the simulation results it has been observed that real power loss reduction, voltage deviation minimization and voltage stability expansion has been achieved.


Keywords


transmission; loss; little golden-mantled flying fox; White-winged chough

Full Text:

PDF

References


1. Sahli Z, Hamouda A, Bekrar A, Trentesaux D. Hybrid PSO-tabu search for the optimal reactive power dispatch problem. In: Proceedings of the IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society; November 2014; Dallas, TX, USA. pp. 3536–3542.

2. Mouassa S, Bouktir T, Salhi A. Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Engineering Science and Technology, an International Journal 2017; 20(3): 885–895. doi: 10.1016/j.jestch.2017.03.006

3. Mandal B, Roy PK. Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. International Journal of Electrical Power & Energy Systems 2013; 53: 123–134. doi: 10.1016/j.ijepes.2013.04.011

4. Khazali AH, Kalantar M. Optimal reactive power dispatch based on harmony search algorithm. International Journal of Electrical Power & Energy Systems 2011; 33(3): 684–692. doi: 10.1016/j.ijepes.2010.11.018

5. Tran HV, Pham TV, Pham LH, et al. Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. Telecommunication Computing Electronics and Control 2019; 17(5): 2517–2526. doi: 10.12928/telkomnika.v17i5.10767

6. Polprasert J, Ongsakul W, Dieu VN. Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electric Power Components and Systems 2016; 44(5): 518–532. doi: 10.1080/15325008.2015.1112449

7. Duong TL, Duong MQ, Phan VD, Nguyen TT. Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. Hindawi Journal of Electrical and Computer Engineering 2020; 2020: 6382507. doi: 10.1155/2020/6382507

8. Illinois Center for a Smarter Electric Grid (ICSEG). Working toward a smarter grid. Available online: https://icseg.iti.illinois.edu/ (accessed on 11 August 2023).

9. Dai C, Chen W, Zhu Y, Zhang X. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Transactions on Power Systems 2009; 24(3): 1218–1231. doi: 10.1109/TPWRS.2009.2021226

10. Subbaraj P, Rajnarayan PN. Optimal reactive power dispatch using self-adaptive real coded Genetic algorithm. Electric Power Systems Research 2009; 79(2): 374–381. doi: 10.1016/j.epsr.2008.07.008

11. Pandya S, Roy R. Particle swarm optimization based optimal reactive power dispatch. In: Proceedings of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT); 5–7 March 2015; Coimbatore, India. pp. 1–5.

12. Illinois Center for a Smarter Electric Grid (ICSEG). The IEEE 57-bus test system. Available online: https://icseg.iti.illinois.edu/ieee-57-bus-system/ (accessed on 11 August 2023).

13. Macur E, Kiraz B. Opposition based binary particle swarm optimization algorithm for feature selection. In: Proceedings of the 2022 Innovations in Intelligent Systems and Applications Conference (ASYU); 7–9 September 2022; Antalya, Turkey. pp. 1–6.

14. Li B, Huang QB, Jiang W. An improved imperialist competitive algorithm inspired by democratic revolution. In: Proceedings of the 2022 China Automation Congress (CAC); 25–27 November 2022; Xiamen, China. pp. 4742–4747.

15. Li M, Mikki S, Uzoma PC, Penkov OV. An efficient method for the experimental characterization of periodic multilayer mirrors: A global optimization approach. IEEE Transactions on Nuclear Science 2023; 70(4): 650–658. doi: 10.1109/TNS.2023.3255892

16. Heaney L, Rosell-Ambal RGB, Tabaranza B, et al. Little golden-mantled flying fox. Available online: https://www.iucnredlist.org/species/18753/22086307 (accessed on 11 August 2023).

17. Boland CRJ, Heinsohn R, Cockburn A. Deception by helpers in cooperatively breeding White-winged choughs and its experimental manipulation. Behavioral Ecology and Sociobiology 1997; 41(4): 251–256. doi: 10.1007/s002650050386

18. Sri Ganapathy Sachchidananda Swamiji. Available online: https://dattapeetham.org/page/view/sri-ganapathy-sachchidananda-swamiji/accessed%20on%2028.10.2022%20at%208.25 (accessed on 11 August 2023).

19. Lotfi H. Optimal sizing of distributed generation units and shunt capacitors in the distribution system considering uncertainty resources by the modified evolutionary algorithm. Journal of Ambient Intelligence and Humanized Computing 2022; 13: 4739–4758. doi: 10.1007/s12652-021-03194-w

20. Lotfi H. Multi-objective network reconfiguration and allocation of capacitor units in radial distribution system using an enhanced artificial bee colony optimization. Electric Power Components and Systems 2021; 49(13–14): 1130–1142. doi: 10.1080/15325008.2022.2049661

21. Lotfi H, Azizivahed A, Shojaei AA, et al. Multi-objective distribution feeder reconfiguration along with optimal sizing of capacitors and distributed generators regarding network voltage security. Electric Power Components and Systems 2021; 49(6–7): 652–668. doi: 10.1080/15325008.2021.2011486




DOI: https://doi.org/10.32629/jai.v6i3.680

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Lenin Kanagasabai

License URL: https://creativecommons.org/licenses/by-nc/4.0