True Power Loss Dwindling and Stability Augmentation by Extreme Learning Machine based Hybrid Lepidoptera-Labidognatha Algorithms and Rhinotia Haemoptera Based Hybrid Canis Aureus Girneys Optimization Algorithm

Abstract

In this paper Extreme Learning Machine based Hybrid Lepidoptera-Labidognatha Algorithms and Rhinotia haemoptera based Hybrid Canis aureus and Girneys optimization algorithm has been applied for solving the power loss lessening problem. In Lepidoptera algorithm Location and stage are rationalized in all iteration. The location modernizing procedure is sustained iteratively up until the end norm is satisfied. And in Labidognatha algorithm every Labidognatha in population, subsequent to the capricious walk step, will have diminutive probability to make a decision on not following its current target and bound away from its existing position. A vibration is spread over the web when a Labidognatha shifts to a new-fangled location. Every vibration seizes the information of one Labidognatha and other Labidognatha can get the information in receipt of the vibration. At its current location it creates vibration when Labidognatha moves to a novel position. In this paper Extreme Learning Machine based Hybrid Lepidoptera and Labidognatha Algorithms is designed to solve the problem. Then in this paper Rhinotia haemoptera based hybrid Canis aureus and Girneys optimization algorithm is modelled for solving the problem. In Canis aureus optimization algorithm deeds of the Canis aureus are used to formulate the algorithm. Through stalking, sneaking and jumping on prey, it hunts. Canis aureus optimization algorithm algorithm imitates the behaviour of Canis aureus as Discover and Stalk segment. Girneys algorithm imitate the deeds of the Girneys have been imitated to formulate the algorithm. Dominant male run the subgroups on the periphery of the central group and communicates messages between the peripheral males and the central. In the projected Rhinotia haemoptera based hybrid Canis aureus and Girneys optimization algorithm Portent Canis aureus will control the quarry expanse by the complete pragmatic from earlobes. This exploit is very alike to the doings of Rhinotia haemoptera drive. Then a modernizing strategy which grounded on the cosine function is used to control the process of the algorithm for evading the local optima. Then Girneys movement are included in the hybridized algorithm. Legitimacy of the Extreme Learning Machine based Hybrid Lepidoptera-Labidognatha algorithms and Rhinotia haemoptera based hybrid Canis aureus and Girneys optimization algorithm is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual power loss lessening is reached. Proportion of actual power loss lessening is augmented

Please cite this article as:

Kanagasabai L. True power loss dwindling and stability augmentation by Extreme Learning Machine based Hybrid Lepidoptera-Labidognatha Algorithms and Rhinotia haemoptera based Hybrid Canis Aureus Girneys Optimization algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2023, no. 5 (110), pp. 4--31. DOI: https://doi.org/10.18698/1812-3368-2023-5-4-31

Литература

[1] Zhu J.Z., Xiong X.F. Optimal reactive power control using modified interior point method. Electr. Power Syst. Res., 2003, vol. 66, iss. 2, pp. 187--192. DOI: https://doi.org/10.1016/S0378-7796(03)00078-6

[2] Quintana V.H., Santos-Nieto M. Reactive-power dispatch by successive quadratic programming. IEEE Trans. Energy Convers., 1989, vol. 4, iss. 3, pp. 425--435. DOI: https://doi.org/10.1109/60.43245

[3] Jan R.-M., Chen N. Application of the fast Newton --- Raphson economic dispatch and reactive power/voltage dispatch by sensitivity factors to optimal power flow. IEEE Trans. Energy Convers., 1995, vol. 10, iss. 2, pp. 293--301. DOI: https://doi.org/10.1109/60.391895

[4] Terra L.D.B., Short M.J. Security-constrained reactive power dispatch. IEEE Trans. Power Syst., 1991, vol. 6, iss. 1, pp. 109--117. DOI: https://doi.org/10.1109/59.131053

[5] Grudinin N. Reactive power optimization using successive quadratic programming method. IEEE Trans. Power Syst., 1998, vol. 13, iss. 4, pp. 1219--1225. DOI: https://doi.org/10.1109/59.736232

[6] Ebeed M., Alhejji A., Kamel S., et al. Solving the optimal reactive power dispatch using marine predators algorithm considering the uncertainties in load and wind-solar generation systems. Energies, 2020, vol. 13, iss. 17, art. 4316. DOI: https://doi.org/10.3390/en13174316

[7] Sahli Z., Hamouda A., Bekrar A., et al. Reactive power dispatch optimization with voltage profile improvement using an efficient hybrid algorithm. Energies, 2018, vol. 11, iss. 8, art. 2134. DOI: https://doi.org/10.3390/en11082134

[8] Davoodi E., Babaei E., Mohammadi-Ivatloo B., et al. A novel fast semidefinite programming-based approach for optimal reactive power dispatch. IEEE Trans. Industr. Inform., 2020, vol. 16, iss. 1, pp. 288--298. DOI: https://doi.org/10.1109/TII.2019.2918143

[9] Bingane C., Anjos M.F., Le Digabel S. Tight-and-cheap conic relaxation for the optimal reactive power dispatch problem. IEEE Trans. Power Syst., 2019, vol. 34, iss. 6, pp. 4684--4693. DOI: https://doi.org/10.1109/TPWRS.2019.2912889

[10] Sahli Z., Hamouda A., Bekrar A., et al. Hybrid PSO-tabu search for the optimal reactive power dispatch problem. IECON, 2014, pp. 3536--3542. DOI: https://doi.org/10.1109/IECON.2014.7049024

[11] Mouassa S., Bouktir T., Salhi A. Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng. Sci. Technol. an Int. J., 2017, vol. 20, iss. 3, pp. 885--895. DOI: https://doi.org/10.1016/j.jestch.2017.03.006

[12] Mandal B., Roy P.K. Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. Int. J. Electr. Power Energy Syst., 2013, vol. 53, pp. 123--134. DOI: https://doi.org/10.1016/j.ijepes.2013.04.011

[13] Khazali H., Kalantar M. Optimal reactive power dispatch based on harmony search algorithm. Int. J. Electr. Power Energy Syst., 2011, vol. 33, iss. 3, pp. 684--692. DOI: https://doi.org/10.1016/j.ijepes.2010.11.018

[14] Tran H.V., Pham T.V., Pham L.H., et al. Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. TELKOMNIKA, 2019, vol. 17, no. 5, pp. 2517--2526. DOI: http://doi.org/10.12928/telkomnika.v17i5.10767

[15] Polprasert J., Ongsakul W., Dieu V.N. Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electr. Power Compon. Syst., 2016, vol. 44, iss. 5, pp. 518--532. DOI: https://doi.org/10.1080/15325008.2015.1112449

[16] Duong T.L., Duong M.Q., Phan V.-D., et al. Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. J. Electr. Comput. Eng., 2020, vol. 2020, art. 6382507. DOI: https://doi.org/10.1155/2020/6382507

[17] Raghuwanshi B.S., Shukla S. Class imbalance learning using UnderBagging based kernelized extreme learning machine. Neurocomputing, 2019, vol. 329, pp. 172--187. DOI: https://doi.org/10.1016/j.neucom.2018.10.056

[18] Yu X., Feng Y., Gao Y., et al. Dual-weighted kernel extreme learning machine for hyperspectral imagery classification. Remote Sens., 2021, vol. 13, iss. 3, art. 508. DOI: https://doi.org/10.3390/rs13030508

[19] Lv F., Han M. Hyperspectral image classification based on multiple reduced kernel extreme learning machine. Int. J. Mach. Learn. Cybern., 2019, vol. 10, no. 6, pp. 3397--3405. DOI: https://doi.org/10.1007/s13042-019-00926-5

[20] Illinois Center for a Smarter Electric Grid (ICSEG). Available at: https://icseg.iti.illinois.edu (accessed: 06.08.2023).

[21] Dai C., Chen W., Zhu Y., et al. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans. Power Syst., 2009, vol. 24, iss. 3, pp. 1218--1231. DOI: https://doi.org/10.1109/TPWRS.2009.2021226

[22] Subbaraj P., Rajnarayan P.N. Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Pow. Syst. Res., 2009, vol. 79, iss. 2, pp. 374--381. DOI: https://doi.org/10.1016/j.epsr.2008.07.008

[23] Pandya S., Roy R. Particle swarm optimization based optimal reactive power dispatch. Proc. ICECCT, 2015. DOI: https://doi.org/10.1109/ICECCT.2015.7225981

[24] Hussain A.N., Abdullah A.A., Neda O.M. Modified particle swarm optimization for solution of reactive power dispatch. Res. J. Appl. Sci. Eng. Technol., 2018, vol. 15, no. 8, pp. 316--327. DOI: http://dx.doi.org/10.19026/rjaset.15.5917

[25] Vishnu M., Kumar T.K.S. An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization. Energies, 2020, vol. 13, iss. 11, art. 2862. DOI: https://doi.org/10.3390/en13112862

[26] Omelchenko I.N., Lyakhovich D.G., Aleksandrov A.A., et al. Development of a design algorithm for the logistics system of product distribution of the mechanical engineering enterprise. Herald of the Bauman Moscow State Technical University, Series Mechanical Engineering, 2020, no. 3 (132), pp. 62--69. DOI: https://doi.org/10.18698/0236-3941-2020-3-62-69

[27] Omelchenko I.N., Zakharov M.N., Lyakhovich D.G., et al. [Organization of logistic systems of scientific productions: scientific research work of the master’s student and evaluation of its results]. Sistemy upravleniya polnym zhiznennym tsiklom vysokotekhnologichnoy produktsii v mashinostroenii: novye istochniki rosta. Mater. III vseros. nauch.-prakt. konf. [Organisation of Logistics Systems for Knowledge-Intensive Industries: Master’s Student Research Work and Evaluation. Proc. III Russ. Sci.-Pract. Conf.]. Moscow, Pervoe ekonomicheskoe izdatelstvo Publ., 2020, pp. 252--256 (in Russ.). DOI: https://doi.org/10.18334/9785912923258.252-256

[28] Omelchenko I.N., Lyakhovich D.G., Aleksandrov A.A., et al. [Problems and organizational and technical solutions of processing management problems of material and technical resources in a design-oriented organization]. Sistemy upravleniya polnym zhiznennym tsiklom vysokotekhnologichnoy produktsii v mashinostroenii: novye istochniki rosta. Mater. III vseros. nauch.-prakt. konf. [Management systems for the full life cycle of high-tech products in mechanical engineering: new sources of growth. Proс. III All-Russ. Sci. Pract. Conf.]. Moscow, Pervoe ekonomicheskoe izdatelstvo Publ., 2020, pp. 257--260 (in Russ.). DOI: https://doi.org/10.18334/9785912923258.257-260

[29] Khunkitti S., Siritaratiwat A., Premrudeepreechacharn S. Multi-objective optimal power flow problems based on slime mould algorithm. Sustainability, 2021, vol. 13, iss. 13, art. 7448. DOI: https://doi.org/10.3390/su13137448

[30] Diab H., Abdelsalam M., Abdelbary A. A multi-objective optimal power flow control of electrical transmission networks using intelligent meta-heuristic optimization techniques. Sustainability, 2021, vol. 13, iss. 9, art. 4979. DOI: https://doi.org/10.3390/su13094979