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Opposition-based chaotic Henry’s law-soluble gas, hybridization of chelonioidea with anthoathecata and vaporization of liquid optimization algorithms for power loss diminution

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Abstract

In this paper opposition-based chaotic Henry's law-soluble gas optimization algorithm (HOC), hybridization of Chelonioidea algorithm with Anthoathecata algorithm (HAC) and vaporization of liquid optimization algorithm (VOL) are projected to solve the real power loss diminishing problem. The proposed opposition-based chaotic Henry's law-based -soluble gas optimization algorithm is based on the Henry’s law of solubility. Opposition-based learning and chaotic are integrated with Henry's law-based -soluble gas optimization algorithm to perk up the performance of the algorithm. Henry’s law states that the total explains about the gas diffuse in the fluid and unswervingly comparative to the fractional force on top of the fluid. Anthoathecata reproduction procedure and foraging behavior of Chelonioidea are mathematically formulated and hybridized to solve the power loss reduction problem. Anthoathecata algorithm (AA) emulates the natural stages of Anthoathecata life cycle to make the exploration further efficient Chelonioidea move in the direction of the food source, which discharge the brawny aroma. Chelonioidea progress may be vigorous and unswerving, but it may also be submissive, aid by the ocean currents. AA is good in exploration and Chelonioidea algorithm is excellent in exploitation. To the hybridization of Chelonioidea algorithm with Anthoathecata algorithm (HAC) an adaptive crossover operator has been integrated to augment the exploration ability. Liquid molecules are taken as individuals of the projected VOL algorithm and concrete platform is imitated as the exploration space. Diminishing of concrete platform wet conditions restructure the liquid accretion from monolayer to a self-locomotion globule. Such a performance is in concurrence with the layout of the VOL algorithm individual’s which vicissitudes to one another. The preeminent approach for renewing the vaporized liquid molecules is by means of the present set of liquid molecules. In this method an arbitrary transformation grounded step size can be considered for conceivable alteration of individuals. The consequent set of molecules is stimulated by accumulation of arbitrary transformation grounded step size and it is multiplied by the similar streamlining prospect called vaporization probability of monolayer and globule vaporization probability proposed opposition-based chaotic Henry’s law-soluble gas optimization algorithm (HOC), hybridization of Chelonioidea algorithm with Anthoathecata algorithm (HAC) and vaporization of liquid optimization (VOL) algorithm are 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 diminishing, voltage divergence curtailing and voltage constancy index augmentation have been attained.

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References

  • Abaci K, Yamaçli V (2017) Optimal reactive-power dispatch using differential search algorithm. Electr Eng 99(1):213–225

    Article  Google Scholar 

  • Carpentier J (1962) Contribution à l’étude du dispatching économique. Bull De La Sociétéfrançaise Des Electriciens 3:431–447

    Google Scholar 

  • Dai C, Chen W, Zhu Y, Zhang X (2009) Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans Power Syst 24(3):1218–1231

    Google Scholar 

  • Das T, Roy R, Mandal K (2021) Solving optimal reactive power dispatch problem with the consideration of load uncertainty using modified JAYA algorithm, pp 1–6. https://doi.org/10.1109/ICAECT49130.2021.9392508

  • Das T, Roy R, Mandal KK (2021) Integrated PV system with optimal reactive power dispatch for voltage security using JAYA algorithm. In: 2021 7th International conference on electrical energy systems (ICEES), pp 56–61. https://doi.org/10.1109/ICEES51510.2021.9383711

  • Dommel HW, Tinney WF (1968) Optimal power flow solutions. IEEE Trans Power Appar Syst 87:1866–1876

    Article  Google Scholar 

  • Duong TL, Duong MQ, Phan VD, Nguyen TT (2020) Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. Hindawi J Electric Comput Eng 200:11. https://doi.org/10.1155/2020/6382507

    Article  Google Scholar 

  • Gelderblom H et al (2011) How water droplets evaporate on a superhydrophobicsubstrate. Phys Rev E 83(2):026306

    Article  Google Scholar 

  • Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015

    Article  Google Scholar 

  • Heidari A, Abbaspour RA, Jordehi AR (2017) Gaussian barebones water cycle algorithm for optimal reactivepower dispatch in electrical power systems. Appl Soft Comput 57:657–671

    Article  Google Scholar 

  • Hong-Kai G, Hai-Ping F (2005) Drop size dependence of the contact angle of nanodroplets. Chin Phys Lett 22(4):787. https://doi.org/10.1016/j.egyr.2020.07.030

    Article  Google Scholar 

  • Hussain AN, Abdullah AA, Neda OM (2018) Modified particle swarm optimization for solution of reactive power dispatch. Res J Appl Sci Eng Technol 15(8):316–327. https://doi.org/10.19026/rjaset.15.5917

    Article  Google Scholar 

  • Illinois Center for a Smarter Electric Grid (ICSEG). Available online: https://icseg.iti.illinois.edu/ieee-30-bussystem/. Accessed 25 Feb 2019

  • Keerio MU, Ali A, Saleem M, Hussain N, Hussain R (2020) Multi-objective optimal reactive power dispatch considering probabilistic load demand along with wind and solar power integration. In: 2020 2nd International conference on smart power and internet energy systems (SPIES), Bangkok, Thailand, pp 502–507. https://doi.org/10.1109/SPIES48661.2020.9243016

  • Khazali H, Kalantar M (2011) Optimal reactive power dispatch based on harmony search algorithm. Int J Electr Power Energy Syst 33(3):684–692

    Article  Google Scholar 

  • Mallipeddi R, Jeyadevi S, Suganthan PN, Baskar S (2012) Efficient constraint handling for optimal reactive power dispatch problems. Swarm Evol Comput 5:28–36

    Article  Google Scholar 

  • Mandal B, Roy PK (2013) Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. Int J Electr Power Energy Syst 53:123–134

    Article  Google Scholar 

  • MATPOWER 4.1 IEEE 30-bus and 118-bus test system. http://www.pserc.cornell.edu/matpower

  • Mouassa S, Bouktir T, Salhi A (2017) Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng Sci Technol Int J 20(3):885–895

    Google Scholar 

  • Mugemanyi S, Qu Z, Rugema FX, Dong Y, Bananeza C, Wang L (2020) Optimal reactive power dispatch using chaotic bat algorithm. IEEE Access 8:65830–65867. https://doi.org/10.1109/ACCESS.2020.2982988

    Article  Google Scholar 

  • Muhammad Y, Khan R, Raja MA, Ullah F, Chaudhary N, He Y (2020a) Solution of optimal reactive power dispatch with FACTS devices: a survey. Energy Rep 6:2211–2229. https://doi.org/10.1016/j.egyr.2020.07.030

    Article  Google Scholar 

  • Muhammad Y, Khan R, Raja MAZ, Ullah F, Chaudhary NI, He Y (2020b) Solution of optimal reactive power dispatch with FACTS devices: a survey. Energy Rep 6:2211–2229. https://doi.org/10.1016/j.egyr.2020.07.030

    Article  Google Scholar 

  • Mukherjee A, Mukherjee V (2015a) Solution of optimal reactive power dispatch by Chaotic Krill Herd algorithm. IET Gen Transm Distrib 9(15):2351–2362

    Article  Google Scholar 

  • Mukherjee A, Mukherjee V (2015b) Solution of optimal reactive power dispatch by chaotic krill herd algorithm. Transm Distrib IET Gen 9:2351–2362

    Article  Google Scholar 

  • Pandya S, Roy R (2015) Particle swarm optimization based optimal reactive power dispatch. In: Proceeding of the IEEE international conference on electrical, computer and communication technologies (ICECCT), pp 1–5

  • Pickover CA (1998) Chaos and fractals: a computer graphical journey. Book. ISBN: 9780080528861

  • Polprasert J, Ongsakul W, Dieu VN (2016) Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electric Power Comp Syst 44(5):518–532

    Article  Google Scholar 

  • Pulluri H, Naresh R, Sharma V (2017) An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow. Appl Soft Comput 54:229–245

    Article  Google Scholar 

  • Roy R, Das T, Mandal KK (2020) Optimal reactive power dispatch for voltage security using JAYA algorithm. In: 2020 International conference on convergence to digital world - Quo Vadis (ICCDW), Mumbai, India, pp 1–6. https://doi.org/10.1109/ICCDW45521.2020.9318700

  • Reddy SS (2014) Faster evolutionary algorithm based optimal power flow using incremental variables. Electr Power Energy Syst 54:198–210

    Article  Google Scholar 

  • Rojas DG, Lezama JL, Villa W (2016) Metaheuristic techniques applied to the optimal reactive power dispatch: a review. IEEE Lat Am Trans 14:2253–2263

    Article  Google Scholar 

  • Sahli Z, Hamouda A, Bekrar A, Trentesaux D (2014) Hybrid PSO-tabu search for the optimal reactive power dispatch problem. In: Proceedings of the IECON 2014–40th annual conference of the IEEE industrial electronics society, Dallas, TX, USA

  • Singh RP, Mukherjee V, Ghoshal SP (2015) Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Appl Soft Comput 29:298–309

    Article  Google Scholar 

  • Subbaraj P, Rajnarayan PN (2009) Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr Power Syst Res 79(2):374–438

    Article  Google Scholar 

  • Surender Reddy S (2017) Optimal reactive power scheduling using cuckoo search algorithm. Int J Electr Comput Eng 7(5):2349–2356

    Google Scholar 

  • Takapoui R, Möhle N, Boyd S, Bemporad A (2017) A simple effective heuristic for embedded mixed-integer quadratic programming. Int J Control 93:1–11

    MathSciNet  Google Scholar 

  • Tran HV, Pham TV, Pham LH, Le NT, Nguyen TT (2019) Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. Telecommun Comput Electron Control 17(5):2517–2526

    Google Scholar 

  • Vishnu M, Sunil K (2020) An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization. Energies 13(2862):2–21. https://doi.org/10.3390/en13112862

    Article  Google Scholar 

  • Wang GG, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157. https://doi.org/10.1016/j.neucom.2015.11.018

    Article  Google Scholar 

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Correspondence to Kanagasabai Lenin.

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Lenin, K. Opposition-based chaotic Henry’s law-soluble gas, hybridization of chelonioidea with anthoathecata and vaporization of liquid optimization algorithms for power loss diminution. Soft Comput 26, 1563–1585 (2022). https://doi.org/10.1007/s00500-021-06710-4

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