Real Power Loss Reduction by Extreme Learning Machine Based Drongos Search Algorithm

Abstract

In this paper the Extreme learning machine (ELM) based Drongos search (DS) algorithm --- ELMDS algorithm --- is applied to solve the power loss lessening problem. Extreme learning machine is applied and learning speed of feed-forward neural networks is composed of input, hidden and output layer. Drongos search algorithm is a modern algorithm which is inspired on the elegance performance of Drongos. In expedition to control obscured place a Drongos j pursuit Drongos i. Formerly Drongos i do not sentient of the existence of the added Drongos, as a consequence to the cause of Drongos j is to accomplish. And in Fiddling "Dron-gos" i differentiate about the presence of Drongos j and it protector its nourishment, Drongos i calculatingly take an impulsive way to sentinel its nourishment. This show is replicated by employing an unpredictable evolution. Then care possibility is replaced by a vibrant care possibility for enrichment, which is adapted by the aptness supremacy of every contender solution. Levy flights are employed as a substitute of unswerving illogical activities to duplicate the dodging performance. In ELMDS algorithm input weight rate and concealed layer inception in ELM are logically optimized by the DS algorithm. Legitimacy of ELMDS algorithm is corroborated in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index. 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 by extreme learning machine based Dron-gos search algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2024, no. 1 (112), pp. 41--62. EDN: FIMVMC

References

[1] Lee K.Y., Park Y.M., Ortiz J.L. A united approach to optimal real and reactive power dispatch. Trans. Power App. Syst., 1985, vol. PAS-104, iss. 5, pp. 1147--1153. DOI: https://doi.org/10.1109/TPAS.1985.323466

[2] Dommel H.W., Tinney W.F. Optimal power flow solutions. IEEE Trans. Power App. Syst., 1968, vol. PAS-87, iss. 10, pp. 1866--1876. DOI: https://doi.org/10.1109/TPAS.1968.292150

[3] Medani K.B.O., Sayah S., Bekrar A. Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr. Power Syst. Res., 2018, vol. 163-B, pp. 696--705. DOI: https://doi.org/10.1016/j.epsr.2017.09.001

[4] Taha I.B.M., Elattar E.E. Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimizer. IET Gener. Transm. Distrib., 2018, vol. 12, iss. 14, pp. 3421--3434. DOI: https://doi.org/10.1049/iet-gtd.2018.0053

[5] Sakr W.S., El-Sehiemy R.A., Azmy A.M. Adaptive differential evolution algorithm for efficient reactive power management. App. Soft Comput., 2017, vol. 53, pp. 336--351. DOI: https://doi.org/10.1016/j.asoc.2017.01.004

[6] Heidari A., Abbaspour R.A., Jordehi A.R. Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. App. Soft Comput., 2017, vol. 57, pp. 657--671. DOI: https://doi.org/10.1016/j.asoc.2017.04.048

[7] Keerio M.U., Ali A., Saleem M., et al. Multi-objective optimal reactive power dis-patch considering probabilistic load demand along with wind and solar power integration. SPIES, 2020, pp. 502--507. DOI: https://doi.org/10.1109/SPIES48661.2020.9243016

[8] Roy R., Das T., Mandal K.K. Optimal reactive power dispatch for voltage security using JAYA algorithm. ICCDW, 2020. DOI: https://doi.org/10.1109/ICCDW45521.2020.9318700

[9] Mugemanyi S., Qu Z., Rugema F.X., et al. Optimal reactive power dispatch using chaotic bat algorithm. IEEE Access, 2020, vol. 8, pp. 65830--65867. DOI: https://doi.org/10.1109/ACCESS.2020.2982988

[10] Sahli Z., Hamouda A., Bekrar A., et al. Hybrid PSO-TABU search for the optimal reactive power dispatch problem. IECON, 2014. 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 (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] Reddy S.S. Optimal reactive power scheduling using cuckoo search algorithm. Int. J. Electr. Comput. Eng., 2017, vol. 7, no. 5, pp. 2349--2356. DOI: http://doi.org/10.11591/ijece.v7i5.pp2349-2356

[32] Reddy S.S. 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