Abstract
In this paper, Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) are applied to solve the power loss lessening problem. Protist Algorithm (PA) is modelled based on the Protist’s natural activities. Protist exists in moist places. The leading nutritious phase is Plasmodium, the energetic and vibrant phase of Protist. In this segment, the organic substance in Protist search for food in surroundings and conceals enzymes for digestion. Natural actions of Otocyon megalotis are emulated to design the OOA approach. In the projected OOA searching of regions in exploration, for foodstuff the Otocyon megalotis mark the prey in the space is indicated as a global exploration. Real power loss reduction and Voltage stability enhancement are the key objectives of the paper. To solve the problem, Protist algorithm (PA) and Otocyon megalotis optimization algorithm. In the course of the migration procedure, the anterior end outspreads and interconnected arterial system that authorize cytoplasm to stream inside. Then, mutation and cross-over probability are employed to augment the performance of the Protist algorithm (PA). With this integration engendering of the population is done. Mutation classes the population exploration agents (PN) in uphill order conferring to the agents appropriateness (fitness) cost. Consequently, the technique splits the organized agents into three fragments rendering to their fitness value. In which PN/3 denotes to the population possess pre-eminent (aptness) fitness values, subsequently with second pre-eminent and poorest aptness (fitness) values. Then, in this paper, Otocyon megalotis optimization algorithm (OOA) is applied for solving the Power loss lessening problem. In the subsequent segment, navigate during the haunt to seal prey previous to the hit was replicated as a local search. In exploration, the data obtained is shared to all the associates of the family unit for continued existence and growth. Examination of the nearby terrain is modelled with reference to the fitness of all entities. Most excellent entity has investigated the majority fascinating terrain and it will be shared with family unit of Otocyon megalotis. Primarily, Otocyon megalotis show that it not involved in hunting. Conversely, as soon as moving near to prey Otocyon megalotis will perform the attack in quick mode. This approach imitated and designed in the local search segment. Authenticity of the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) is corroborated in 23 benchmark functions, IEEE 30, 57, 300 and 354 test systems. Power Loss reduction achieved with voltage stability enhancement. Real power loss reduction attained. Both the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) performed well in solving the Power loss reduction problem.
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References
Abbasi S (2022) The sustainable supply chain of CO2 emissions during the coronavirus disease (COVID-19) pandemic. J Ind Eng Int 17(4):83–108. https://doi.org/10.30495/JIEI.2022.1942784.1169
Abbasi S, Choukolaei HA (2023) A systematic review of green supply chain network design literature focusing on carbon policy. Decis Anal J 6:100189. https://doi.org/10.1016/j.dajour.2023.100189
Abbasi S, Erdebilli B (2023) Green Closed-Loop Supply Chain Networks’ Response to Various Carbon Policies during COVID-19. Sustainability 15:3677. https://doi.org/10.3390/su15043677
Abbasi S, Daneshmand-Mehr M, Kanafi A (2022a) Designing sustainable recovery network of end-of-life product during the COVID-19 pandemic: a real and applied case study. Discret Dyn Nat Soc 2022:1026–0226. https://doi.org/10.1155/2022/6967088
Abbasi S, Khalili HA, Daneshmand-Mehr M, Hajiaghaei-Keshteli M (2022b) Performance measurement of the sustainable supply chain during the COVID-19 pandemic: a real-life case study. Found Comput Decis Sci 47(4):327–358
Abbasi S, Daneshmand-Mehr M, GhaneKanafi A (2023a) Green closed-loop supply chain network design during the coronavirus (COVID-19) pandemic: a case study in the Iranian automotive industry. Environ Model Assess 28:69–103
Abbasi S, Daneshmand-Mehr M, Kanafi A (2023b) Designing a tri-objective, sustainable, closed-loop, and multi- echelon supply chain during the covid-19 and lockdowns. Found Comput Decis Sci 48(3):1–32
Abd-El Wahab AM, Kamel S, Hassan MH, Mosaad MI, AbdulFattah TA (2022) Optimal reactive power dispatch using a chaotic turbulent flow of water-based optimization algorithm. Mathematics 10(3):346. https://doi.org/10.3390/math10030346
Abdel-Fatah S, Ebeed M, Kamel S (2019) Optimal reactive power dispatch using modified sine cosine algorithm. In: 2019 International conference on innovative trends in computer engineering (ITCE), Aswan, Egypt, 2019, pp 510–514. https://doi.org/10.1109/ITCE.2019.8646460
Anil Kumar PG, ArunaJeyanthy P, Devaraj D (2022) Hybrid CAC-DE in optimal reactive power dispatch (ORPD) for renewable energy cost reduction. Sustain Comput Inform Syst 35:100688. https://doi.org/10.1016/j.suscom.2022.100688
Bibi F, Pante M, Souron A, Stewart K, Varela S, Werdelin L, Boisserie J-R, Fortelius M, Hlusko L, Njau J, de la Torre I (2018) Paleoecology of the Serengeti during the Oldowan-Acheulean transition at Olduvai Gorge, Tanzania: the mammal and fish evidence. J Hum Evol 120:48–75. https://doi.org/10.1016/j.jhevol.2017.10.009
Brown MW et al (2018) Phylogenomics places orphan protistan lineages in a novel eukaryotic super-group. Genome Biol Evol 10(2):427–433. https://doi.org/10.1093/gbe/evy014
Illinois Center for a Smarter Electric Grid (ICSEG) (2022) https://icseg.iti.illinois.edu/ieee-30-bussystem/. Accessed 25 Feb 2022
Chen G, Liu L, Guo Y (2016) Shanwai Huang, Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems. COMPEL Int J Comput Math Electr Electron Eng 35(1):350–372
Constante FSG, López JC, Rider MJ (2021) Optimal reactive power dispatch with discrete controllers using a branch-and-bound algorithm: a semidefinite relaxation approach. IEEE Trans Power Syst 36(5):4539–4550. https://doi.org/10.1109/TPWRS.2021.3056637
Duong TL, Duong MQ, Phan V-D, Nguyen TT (2020) Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. J Electr Comput Eng 2020:Article ID 6382507. https://doi.org/10.1155/2020/6382507
Duong V-T, Nguyen T-T, Duong T-L, Truong A-V (2021) Optimal reactive power dispatch using sunflower algorithm. In: 2021 International conference on system science and engineering (ICSSE), Ho Chi Minh City, Vietnam, 2021, pp 422–426. https://doi.org/10.1109/ICSSE52999.2021.9538483
Elsayed SK, Kamel S, Selim A, Ahmed M (2021) An improved heap-based optimizer for optimal reactive power dispatch. IEEE Access 9:58319–58336. https://doi.org/10.1109/ACCESS.2021.3073276
Hassan MH, Kamel S, El-Dabah MA, Khurshaid T, Domínguez-García JL (2021) Optimal reactive power dispatch with time-varying demand and renewable energy uncertainty using Rao-3 algorithm. IEEE Access 9:23264–23283. https://doi.org/10.1109/ACCESS.2021.3056423
Kanagasabai L (2023) Real power loss reduction by Maine Coon and Perognathinae based optimization algorithm. Herald Bauman Moscow State Tech Univ Ser Nat Sci 3(108):61–84. https://doi.org/10.18698/1812-3368-2023-3-61-84
Khan NH, Jamal R, Ebeed M, Kamel S, Zeinoddini-Meymand H, Zawbaa HM (2022) Adopting scenario-based approach to solve optimal reactive power dispatch problem with integration of wind and solar energy using improved marine predator algorithm. Ain Shams Eng J 13(5):101726. https://doi.org/10.1016/j.asej.2022.101726
Khazali AH, Kalantar M (2011) Optimal reactive power dispatch based on harmony search algorithm. Int J Electr Power Energy Syst 33:684–692
Lenin K (2022) Novel Western Jackdaw search, antrostomus swarm and indian ethnic vedic teaching—inspired optimization algorithms for real power loss diminishing and voltage consistency growth. Int J Syst Assur Eng Manag 13(6):2895–2919
Mandal B, Roy PK (2013) Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. Int J Electr Power Energy Syst 53:123–134
Mouassa S, Bouktir T, Salhi A (2017) Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng Sci Technol Int J 20(3):885–895
Nagarajan K, Parvathy AK, Arul R (2020) Multi-objective optimal reactive power dispatch using levy interior search algorithm. Int J Electr Eng Inform 12:547–570. https://doi.org/10.15676/ijeei.2020.12.3.8
Niu M, Xu NZ, Dong HN, Ge YY, Liu YT, Ngin HT (2021) Adaptive range composite differential evolution for fast optimal reactive power dispatch. IEEE Access 9:20117–20126. https://doi.org/10.1109/ACCESS.2021.3053640
Nuaekaew K, Artrit P, Pholdee N, Bureerat S (2017) Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer. Expert Syst Appl 87:79–89
Sharifzadeh H, Jazaeri M (2009) Optimal reactive power dispatch based on particle swarm optimization considering FACTS devices. In: 2009 International conference on sustainable power generation and supply, Nanjing, China, 2009, pp. 1–7. https://doi.org/10.1109/SUPERGEN.2009.5347956.
Singh P, Arya R, Titare L, Arya L (2021) Optimal load shedding to avoid risks of voltage collapse using black hole algorithm. J Inst Eng (India) Ser B 102:261–276. https://doi.org/10.1007/s40031-021-00543-3
The IEEE 57-Bus Test System (2023) http://www.ee.washington.edu/research/pstca/pf57/pg_tca57bus.htm
Tran HV, Pham TV, Pham LH, Le NT, Nguyen TT (2019) Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. Telecommun Comput Electron Control 17(5):2517–2526
Tudose AM, Picioroaga II, Sidea DO, Bulac C (2021) Solving single- and multi-objective optimal reactive power dispatch problems using an improved Salp swarm algorithm. Energies 14:1222 (1–20)
Venkataramana A, Carr J, Ramshaw RS (1987) Optimal reactive power allocation. IEEE Trans Power Syst 2(1):138–144. https://doi.org/10.1109/TPWRS.1987.4335088
Zhang Y-M, Chen C-R, Lee C-Y (2018) Solution of the optimal reactive power dispatch for power systems by using novel charged system search algorithm. In: 2018 7th International Symposium on Next Generation Electronics (ISNE), Taipei, Taiwan, pp 1-4, https://doi.org/10.1109/ISNE.2018.8394748
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Kanagasabai, L. Real power loss reduction by Protist and Otocyon megalotis optimization algorithms. Soft Comput 28, 3107–3121 (2024). https://doi.org/10.1007/s00500-023-09275-6
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DOI: https://doi.org/10.1007/s00500-023-09275-6