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Real Power Loss Reduction by Enhanced Russian Haliaeetus Pelagicus Optimization

Авторы: Kanagasabai L. Опубликовано: 05.03.2023
Опубликовано в выпуске: #1(106)/2023  
DOI: 10.18698/1812-3368-2023-1-64-81

 
Раздел: Математика и механика | Рубрика: Математическое моделирование, численные методы и комплексы программ  
Ключевые слова: optimal reactive power, transmission loss, Russian Haliaeetus pelagicus

Abstract

In this paper Enhanced Russian Haliaeetus pelagicus Optimization Algorithm is applied for solving the Power loss lessening problem. Russian Haliaeetus pelagicus Optimization Algorithm is modeled based on the natural deeds of Russian Haliaeetus pelagicus. A spiral trajectory for exploration and a straight-line lane for assails done by Russian Haliaeetus pelagicus for hunting. It shows proclivity to sail in preliminary phase of hunting and efficiently changeover to further proclivity to assail in the concluding phases. Russian Haliaeetus pelagicus conserve proclivity for both sail and assail in each instant of the voyage. Sail vector is computed based on the assail vector. Sail vector is a tangent to the loop and vertical to the assail vector. The sail can be linear pace of Russian Haliaeetus pelagicus in comparison the prey. The sail vector in n-dimensions is situated within the tangent plane in loop in order compute the sail vector. In Enhanced Russian Haliaeetus pelagicus Optimization Algorithm exterior archive, prey precedence condition, and picking of prey are added through multi-objective mode. The fundamental plan is to keep capable solutions in an exterior archive and modernize when procedure continues. Exploration agents are moved in the direction of the stored entities. If the new-fangled solution is conquered by one or more of the present archives' entities, then the new-fangled solution is removed. If the new-fangled solution is not ruled over the present entities of the stored one and the records are not occupied, basically append the new-fangled position to the store. Prudence of the Enhanced Russian Haliaeetus pelagicus Optimization Algorithm is corroborated in IEEE 30 bus system (with and devoid of L-index). True power loss lessening is reached. Ratio of true power loss lessening is augmented

Please cite this article as:

Kanagasabai L. Real power loss reduction by Enhanced Russian Haliaeetus pelagicus Optimization Algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2023, no. 1 (106), pp. 64--81. DOI: https://doi.org/10.18698/1812-3368-2023-1-64-81

Литература

[1] Carpentier J. Contribution a l’etude du dispatching economique. Bull. de la Societe Francaise des Electriciens, 1962, vol. 3, pp. 431--447.

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

[3] Takapoui R., Mohle N., Boyd S., et al. A simple effective heuristic for embedded mixed-integer quadratic programming. ACC, 2016. DOI: https://doi.org/10.1109/ACC.2016.7526551

[4] Abaci K., Yamacli V. Optimal reactive-power dispatch using differential search algorithm. Electr. Eng., 2017, vol. 99, no. 1, pp. 213--225. DOI: https://doi.org/10.1007/s00202-016-0410-5

[5] Pulluri H., Naresh R., Sharma V. An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow. Appl. Soft Comput., 2017, vol. 54, pp. 229--245. DOI: https://doi.org/10.1016/j.asoc.2017.01.030

[6] Heidari A., Abbaspour R.A., Jordehi A.R. Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl. 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 dispatch 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] Khalilpourazari S., Pasandideh S.H.R. Sine-cosine crow search algorithm: theory and application. Neural Comput & Applic., 2019, vol. 32, no. 12, pp. 7725--7742. DOI: https://doi.org/10.1007/s00521-019-04530-0

[18] Faramarzi A., Heidarinejad M., Stephens B., et al. Equilibrium optimizer: a novel optimization algorithm. Knowl.-Based Syst., 2020, vol. 191, art. 105190. DOI: https://doi.org/10.1016/j.knosys.2019.105190

[19] Chakri A., Khelif R., Benouaret M., et al. New directional bat algorithm for continuous optimization problems. Expert Syst. Appl., 2017, vol. 69, pp. 159--175. DOI: https://doi.org/10.1016/j.eswa.2016.10.050

[20] Mirjalili S., Gandomi A.H. Chaotic gravitational constants for the gravitational search algorithm. Appl. Soft Comput., 2017, vol. 53, pp. 407--419. DOI: https://doi.org/10.1016/j.asoc.2017.01.008

[21] Ahmadi A., Tiruta-Barna L., Capitanescu F., et al. An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: application to process eco-design. Comput. Chem. Eng., 2016, vol. 87, pp. 95--110. DOI: https://doi.org/10.1016/j.compchemeng.2015.12.008

[22] Chen L., Li Q., Zhao X., et al. Multi-population coevolutionary dynamic multi-objective particle swarm optimization algorithm for power control based on improved crowding distance archive management in CRNs. Comput. Commun., 2019, vol. 145, pp. 146--160. DOI: https://doi.org/10.1016/j.comcom.2019.06.009

[23] Deb K., Agrawal S., Pratap A., et al. Fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol. 1917. Berlin, Heidelberg, Springer, 2000, pp. 849--858. DOI: https://doi.org/10.1007/3-540-45356-3_83

[24] Illinois Center for a Smarter Electric Grid (ICSEG). Available at: https://icseg.iti.illinois.edu (accessed: 25.02.2021).

[25] 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

[26] 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

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

[28] 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

[29] 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, pp. 2--21. DOI: https://doi.org/10.3390/en13112862