Real Power Loss Reduction by Basketball League Algorithm

Abstract

In this paper Basketball League (BL) algorithm is applied to solve the power loss lessening problem. BL algorithm has been modelled based on the actions of BL squad. Basketball League game characteristically entails of 4 quarters and each one is ten minutes. Subsequently double quarters the squads shift sides on the courtyard. The squad with extra points at the conclusion of the 4 quarters triumphs the competition. If the total is tied at the conclusion of ruling time, the play will be preceded to additional time period. A bellicose competitor can possess the ball stirring round the court by drooling or fleeting the basketball to a coplayer. In a ball drool, a competitor requests to rebound the basketball counter to the base incessantly practice single hand in a period. Using the both hands concurrently drool the basketball two times previously it rebounds on one occasion establishes a twofold drool defilement, which finishes in relinquishing control to the rival squad. Basketball team possess 12 players, with 5 players on the courtyard with Limit-less replacements are permissible. Players in BL are alienated into consistently fragmented squads grounded upon age, capability, and proficiency. Elucidation has been generated grounded on the squad, players, trainer, and replacement approach. Principally fitness utility for every solution will be calculated and the consequence of competition amongst any two squads playing in BL is anonymous; some squad can triumph at the conclusion. Obviously when squad "i" win over squad "j" it's owed to the influence of the winning squad correspondingly it will be pathetic point for the dropping squad. Initial solution of the problem is engendered and squads are initialized contingent on the creation of the squad with strategies. Initial solution of the problem is engendered and squads are initialized contingent on the creation of the squad with strategies. Trainers are playing principal part in providing effective contribution to the squad. Correspondingly trainers modify the strategies throughout the competition in the direction of competition wining. Rendering to the performance in the specific period elevation and relegation of the squads will be present. Supremely accomplished squads will be endorsed to eldest competition and deprived performed squad will be downgraded to junior division league. Legitimacy of BL algorithm is corroborated in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the power 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 Basketball League algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2024, no. 3 (114), pp. 92--114. EDN: RGSEIK

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 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] Grundman A.H. The golden age of amateur basketball. University of Nebraska Press, 2004.

[18] Brown D.H. A basketball handbook. AuthorHouse, 2007.

[19] Forrest C.A. All you wanted to know about basketball. Sterling Publ., 1991.

[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, 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 TechnicalUniversity, 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. 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