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Real Power Loss Reduction by Maine Coon and Perognathinae Based Optimization Algorithm

Авторы: Kanagasabai L. Опубликовано: 04.07.2023
Опубликовано в выпуске: #3(108)/2023  
DOI: 10.18698/1812-3368-2023-3-61-84

 
Раздел: Математика и механика | Рубрика: Вычислительная математика  
Ключевые слова: optimal reactive power, transmission loss, Maine Coon, Perognathinae

Abstract

This paper proposes Maine Coon and Perognathinae based optimization (MPO) algorithm for solving the power loss lessening problem. Usual behaviour between Maine Coon and Perognathinae is imitated to formulate the MPO algorithm. In the proposed MPO, the crusade of Maine Coon towards Perognathinae as well as the spurt of Perognathinae in the direction of anchorages is replicated. Proposed MPO is population-based procedure which is premeditated by imitating the natural actions of a Maine Coon assaults on Perognathinae and absconding of Perognathinae to the anchorage. The exploration agents in the projected MPO algorithm are alienated into two clusters of Maine Coon’s and Perognathinae that examine the problem exploration space with arbitrary activities. The projected MPO algorithm apprises population associates in two segments. In the principal segment, the crusade of Maine Coon’s in the direction of Perognathinae is modelled, and in the subsequent segment, the absconding behaviour of Perognathinae to anchorages to protect its life is designed. From a scientific fact of opinion, every associate of the populace is a recommended solution to the problem. In detail, an associate of the population postulates standards for the problem parameters rendering to its location in the exploration space. Proposed MPO algorithm is appraised 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 Maine Coon and Perognathinae based optimization algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2023, no. 3 (108), pp. 61--84. DOI: https://doi.org/10.18698/1812-3368-2023-3-61-84

Литература

[1] Lee K., Park Y.M., Ortiz J.L. Fuel-cost minimization for both real and reactive-power dispatches. IEE Proceedings C, 1984, vol. 131, iss. 3, pp. 85--93. DOI: https://doi.org/10.1049/ip-c.1984.0012

[2] Deeb N., Shahidehpour M. An efficient technique for reactive power dispatch using a revised linear programming approach. Electr. Power Syst. Res., 1988, vol. 15, iss. 2, pp. 121--134. DOI: https://doi.org/10.1016/0378-7796(88)90016-8

[3] Bjelogrlic M., Calovic M.S., Ristanovic P., et al. Application of Newton’s optimal power flow in voltage/reactive power control IEEE Trans. Power Syst., 1990, vol. 5, iss. 4, pp. 1447--1454. DOI: https://doi.org/10.1109/59.99399

[4] Granville S. Optimal reactive dispatch through interior point methods. IEEE Trans. Power Syst., 1994, vol. 9, iss. 1, pp. 136--146. DOI: https://doi.org/10.1109/59.317548

[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] Mavaddat N., Michailidou K., Dennis J., et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet., 2019, vol. 104, iss. 1, pp. 21--34. DOI: https://doi.org/10.1016/j.ajhg.2018.11.002

[7] Kanimozhi U., Ganapathy S., Manjula D., et al. An intelligent risk prediction system for breast cancer using fuzzy temporal rules. Natl. Acad. Sci. Lett., 2019, vol. 42, no. 3, pp. 227--232. DOI: https://doi.org/10.1007/s40009-018-0732-0

[8] Gupta K., Janghel R. Dimensionality reduction-based breast cancer classification using machine learning. In: Verma N., Ghosh A. (eds). Computational Intelligence. Theories, Applications and Future Directions. Vol. 798. Singapore, Springer, 2019, pp. 133--146. DOI: https://doi.org/10.1007/978-981-13-1132-1_11

[9] Sangaiah I., Vincent Antony Kumar A. Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: application to breast cancer prediction. Cluster Comput., 2019, vol. 22, no. S3, pp. 6899--6906. DOI: https://doi.org/10.1007/s10586-018-1702-5

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] 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

[16] Bhattacharya A., Chattopadhyay P.K. Solution of optimal reactive power flow using biogeography-based optimization. Int. J. Electr. Electron. Eng., 2010, vol. 4, no. 3, pp. 621--629.

[17] Duman S., Sönmez Y., Guvenç U., et al. Optimal reactive power dispatch using a gravitational search algorithm. IET Gener. Transm. Distrib., 2012, vol. 6, iss. 6, pp. 563--576. DOI: https://doi.org/10.1049/iet-gtd.2011.0681

[18] Li Wu. Optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and lévy walk. J. Mod. Power Syst. Clean Energy, 2014, vol. 2, no. 4, pp. 308--318. DOI: https://doi.org/10.1007/s40565-014-0076-9

[19] MATPOWER 4.1 IEEE 30-bus and 118-bus test system. 2019. Available at: https://matpower.org

[20] 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

[21] 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

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

[23] 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

[24] 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

[25] Basu M. Improved particle swarm optimization for global optimization of unimodal and multimodal functions. J. Inst. Eng. India Ser. B., 2016, vol. 97, no. 4, pp. 525--535. DOI: https://doi.org/10.1007/s40031-015-0204-6

[26] Arya R., Purey P. Active power rescheduling for avoiding voltage collapse using modified bare bones particle swarm optimization. J. Inst. Eng. India Ser. B, 2016, vol. 97, no. 2, pp. 109--113. DOI: https://doi.org/10.1007/s40031-015-0209-1

[27] Jain N.K., Nangia U., Jain J. A review of particle swarm optimization. J. Inst. Eng. India Ser. B, 2018, vol. 99, no. 4, pp. 407--411. DOI: https://doi.org/10.1007/s40031-018-0323-y

[28] Kela K.B., Arya L.D. Reliability optimization of radial distribution systems employing differential evolution and bare bones particle swarm optimization. J. Inst. Eng. India Ser. B, 2014, vol. 95, no. 3, pp. 231--239. DOI: https://doi.org/10.1007/s40031-014-0094-z

[29] Jain N.K., Nangia U., Jain J. Economic load dispatch using adaptive social acceleration constant based particle swarm optimization. J. Inst. Eng. India Ser. B, 2018, vol. 99, no. 5, pp. 431--439. DOI: https://doi.org/10.1007/s40031-018-0322-z

[30] Verma H.K., Pal S. Modified sigmoid function based gray scale image contrast enhancement using particle swarm optimization. J. Inst. Eng. India Ser. B, 2016, vol. 97, no. 2, pp. 243--251. DOI: https://doi.org/10.1007/s40031-014-0175-z

[31] Chiu C.C., Chen C.H., Fan Y.S. Image reconstruction of a buried conductor by modified particle swarm optimization. IETE J. Res., 2012, vol. 58, no. 4, pp. 284--291.

[32] Dash P.K., Panigrahi B.K., Hasan S. Hybrid particle swarm optimization and unscented filtering technique for estimation of non-stationary signal parameters. IETE J. Res., 2009, vol. 55, no. 6, pp. 266--274.

[33] Fahimeh Z., Mehdi G., Mehdi H. Optimizing radio frequency identification networks planning by using particle swarm optimization algorithm with fuzzy logic controller and mutation. IETE J. Res., 2017, vol. 63, iss. 5, pp. 728--735. DOI: https://doi.org/10.1080/03772063.2015.1083905

[34] Biswal P., Dash P.K., Panigrahi B.K. Time frequency analysis and non-stationary signal classification using PSO based fuzzy C-means algorithm. IETE J. Res., 2007, vol. 53, iss. 5, pp. 441--450. DOI: https://doi.org/10.1080/03772063.2007.10876159

[35] Chaitanya R.K., Raju G.S.N., Raju K.V.S.N., et al. Antenna pattern synthesis using the quasi-Newton method, firefly and particle swarm optimization techniques. IETE J. Res., 2019, vol. 68, iss. 2, pp. 1148--1156. DOI: https://doi.org/10.1080/03772063.2019.1643263

[36] Raval P.D., Pandya A.S. A hybrid PSO-ANN-based fault classification system for EHV transmission lines. IETE J. Res., 2022, vol. 68, iss. 4, pp. 3086--3099. DOI: https://doi.org/10.1080/03772063.2020.1754299

[37] Kumar P., Silambarasan K. Enhancing the performance of healthcare service in IoT and cloud using optimized techniques. IETE J. Res., 2019, vol. 68, iss. 2, pp. 1475--1484. DOI: https://doi.org/10.1080/03772063.2019.1654934

[38] Barnali S., Kumar J.A., Satchidananda D. Adaptive improved binary PSO-based learnable Bayesian classifier for dimensionality reduced microarray data. Int. J. Med. Eng. Inform., 2019, vol. 11, iss. 3, pp. 265--285. DOI: https://doi.org/10.1504/IJMEI.2019.10020712

[39] Ayoubi A., Sanie M.S., Kazemi M. Synchronisation of SA and AV node oscillators using PSO optimised RBF-based controllers and comparison with adaptive control. Int. J. Med. Eng. Inform., 2016, vol. 8, no. 3, pp. 210--224. DOI: https://doi.org/10.1504/IJMEI.2016.077438

[40] Mishra A.K., Singh B. Performance optimization of PV-powered SRM-driven water pump using modified CUK converter. J. Inst. Eng. India Ser. B., 2019, vol. 100, no. 3, pp. 249--258. DOI: https://doi.org/10.1007/s40031-019-00381-4

[41] Singh S., Singh B., Bhuvaneswari G., et al. Unity power factor operated PFC converter based power supply for computers. J. Inst. Eng. India Ser. B, 2018, vol. 99, no. 1, pp. 49--60. DOI: https://doi.org/10.1007/s40031-017-0303-7

[42] Singh N., Kumar Y. Multiobjective economic load dispatch problem solved by new PSO. Adv. Electr. Electron. Eng., 2015, vol. 2015, art. 536040. DOI: https://doi.org/10.1155/2015/536040

[43] Gupta A.R., Kumar A. Performance analysis of radial distribution systems with UPQC and D-STATCOM. J. Inst. Eng. India Ser. B., 2017, vol. 98, no. 4, pp. 415--422. DOI: https://doi.org/10.1007/s40031-016-0254-4

[44] Hazarika D., Hussain S.A. A voltage stability index for an interconnected power system based on network partitioning technique. J. Inst. Eng. India Ser. B., 2018, vol. 99, no. 6, pp. 565--573. DOI: https://doi.org/10.1007/s40031-018-0353-5

[45] Teeparthi K., Kumar D.M.V. An improved artificial physics optimization algorithm approach for static power system security analysis. J. Inst. Eng. India Ser. B., 2020, vol. 101, no. 4, pp. 347--359. DOI: https://doi.org/10.1007/s40031-020-00457-6

[46] Kumar Sharma A., Murty V.V.S.N. Analysis of mesh distribution systems considering load models and load growth impact with loops on system performance. J. Inst. Eng. India Ser. B., 2014, vol. 95, no. 4, pp. 295--318. DOI: https://doi.org/10.1007/s40031-014-0108-x

[47] Chejarla M.K.D., Matam S.K. Multiple solutions for optimal PMU placement using a topology-based method. J. Inst. Eng. India Ser. B., 2021, vol. 102, no. 2, pp. 249--259. DOI: https://doi.org/10.1007/s40031-020-00532-y

[48] Kumar J., Kumar N. FACTS devices impact on congestion mitigation of power system. J. Inst. Eng. India Ser. B., 2020, vol. 101, no. 3, pp. 239--254. DOI: https://doi.org/10.1007/s40031-020-00450-z

[49] Gupta A.R., Kumar A. Comparison of deterministic and probabilistic radial distribution systems load flow. J. Inst. Eng. India Ser. B., 2017, vol. 98, no. 6, pp. 547--556. DOI: https://doi.org/10.1007/s40031-017-0288-2

[50] Kanagasabai L. Real power loss reduction by North American sapsucker algorithm. Int. J. Syst. Assur. Eng. Manag., 2022, vol. 13, no. 1, pp. 143--153. DOI: https://doi.org/10.1007/s13198-021-01155-2

[51] Lenin K. Real power loss reduction by Duponchelia fovealis optimization and enriched squirrel search optimization algorithms. Soft. Comput., 2020, vol. 24, no. 23, pp. 17863--17873. DOI: https://doi.org/10.1007/s00500-020-05036-x

[52] Lenin K. Solving optimal reactive power problem by Alaskan Moose Hunting, Larus livens and Green Lourie Swarm optimization algorithms. Ain Shams Eng. J., 2020, vol. 11, iss. 4, pp. 1227--1235. DOI: https://doi.org/10.1016/j.asej.2020.03.019

[53] Omelchenko I.N., Lyakhovich D., 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

[54] Omelchenko I., Zakharov M., Lyakhovich D., 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. [Management Systems for the Full Life Cycle of High-Tech Products in Mechanical Engineering: New Sources of Growth. Materials of the III All-Russ. Sci.-Pract. Conf.]. Moscow, Pervoe ekonomicheskoe izdatelstvo Publ., 2020, pp. 252--256 (in Russ.). DOI: https://doi.org/10.18334/9785912923258.252-256

[55] Omelchenko I., Lyakhovich D., Aleksandrov 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. Materials of the 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