Real Power Loss Reduction and Voltage Stability Enrichment by Extreme Learning Machine Based Phoenicoparrus Search, Quantum-Inspired Myrmicinae, Herpestes, Philander Olrogi and Chaotic Based Simien Fox Optimization Algorithms

Abstract

In this paper Extreme learning machine based Phoenicoparrus search (ELMPS) optimization algorithm, quantum-inspired Myrmicinae evolutionary algorithm (QIMEA), Herpestes optimization (HO) algorithm, Philander olrogi optimization (PO) algorithm and Chaotic based Simien Fox optimization (CSFO) algorithm has been applied to solve the power loss lessening problem. Phoenicoparrus search (PS) optimization algorithm is stimulated by the Phoenicoparrus itinerant and scavenging behaviour. Then the extreme learning machine based Phoenicoparrus search (ELMPS) optimization algorithm is designed. Quantum-inspired Myrmicinae evolutionary algorithm (QIMEA) design is imitated by the actions of Myrmicinae. In HO algorithm with the reference to the rate of breed centre of group and sex, Herpestes group individuals will perform the movement. Then Philander olrogi optimization algorithm has been designed based on the track and preying actions of Philander olrogi. Based on the natural actions of Simien Fox proposed CSFO algorithm is designed. Chaotic sequences are integrated into the Simien Fox optimization algorithm and it will enhance the exploration and exploitation. Legitimacy of proposed algorithms is corroborated in standard IEEE test systems. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained

Please cite this article as:

Kanagasabai L. Real power loss reduction and voltage stability enrichment by Extreme learning machine based Phoenicoparrus search, quantum-inspired Myrmicinae, Herpestes, Philander olrogi and chaotic based Simien Fox optimization algorithms. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2024, no. 5 (116), pp. 33--55. EDN: OSBUFS

References

[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 dis-patch 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 pro-gramming 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, 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 quasioppositional 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, 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, iss. 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 (in Russ.). 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. Mat. III Vseros. nauch.-prakt. konf. [Management systems for the full life cycle of high-tech products in mechanical engineering: new sources of growth. Mat. 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

[31] Surender R.S. Optimal reactive power scheduling using cuckoo search algorithm. IJECE, 2017, vol. 7, no. 5, pp. 2349--2356. DOI: http://doi.org/10.11591/ijece.v7i5.pp2349-2356

[32] Reddy S.S., Bijwe P.R., Abhyankar A.R. Faster evolutionary algorithm based optimal power flow using incremental variables. Int. J. Electr. Power Energy Syst., 2014, vol. 54, pp. 198--210. DOI: https://doi.org/10.1016/j.ijepes.2013.07.019

[33] Davidchack R.L., Lai Y.C., Klebanoff A., et al. Towards complete detection of unstable periodic orbits in chaotic systems. Phys. Lett. A, 2001, vol. 287, iss. 1-2, pp. 99--104. DOI: https://doi.org/10.1016/S0375-9601(01)00463-7

[34] Inoue K., Ohya M., Sato K. Application of chaos degree to some dynamical systems. Chaos Solitons Fractals, 2000, vol. 11, iss. 9, pp. 1377--1385. DOI: https://doi.org/10.1016/S0960-0779(99)00050-8