Factual Power Loss Lessening by Enhanced Synthetic Biome Optimization and Green Algae Algorithms

Authors: Kanagasabai L. Published: 23.06.2022
Published in issue: #3(102)/2022  
DOI: 10.18698/1812-3368-2022-3-28-42

Category: Mathematics and Mechanics | Chapter: Computational Mathematics  
Keywords: оptimization, Transmission loss, Green Algae, Synthetic Biome, algorithm


In this paper Enhanced Synthetic Biome Optimization (ESBO) and Green Algae algorithm (GAA) is designed to abridge the power loss. Synthetic Biome Optimization (SBO) algorithm is a nature-inspired optimization algorithm, stimulated by the stream of energy in a biome on the world. The biome can be enunciated as a cluster of existing entities living in a certain province and the biome outlines the associations among them. The deprived entity (creator) is rationalized by the upper and low borders of exploration space and the pre-eminent entity (putrefaction). Levy flight applied to augment the exploration and imitate the food probing process of many faunae. In order to augment the convergence characteristics of the SBO algorithm, sine-cosine functions has been incorporated in the technique. This augmentation will stimulate divergent solutions and modifies in the direction of the distinguished prospective solution in ESBO. Proposed GAA approach imitates growing, reproduction deeds of green algae in sunlight. Green algae live in the shape of algal colonies which consist of algal cells. When green algae in least amount of sunlight it will be small size, energy and particularly starvation level will be high, but it will attempt to acclimatize by using adaptation probability in the ambiance where it positioned. Enhanced Synthetic Biome Optimization and GAA appraised in IEEE 57 and 300 bus systems. Assessment with other techniques has been done. Projected ESBO and GAA approaches abridged the power loss meritoriously

Please cite this article as:
Kanagasabai L. Factual power loss lessening by Enhanced Synthetic Biome Optimization and Green Algae algorithms. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2022, no. 3 (102), pp. 28--42. DOI: https://doi.org/10.18698/1812-3368-2022-3-28-42


[1] Davoodi E., Babaei E., Mohammadi-Ivatloo B., et al. A novel fast semidefinite programming-based approach for optimal reactive power dispatch. IEEE Trans. Ind. Informat., 2020, vol. 16, iss. 1, pp. 288--298. DOI: https://doi.org/10.1109/TII.2019.2918143

[2] Zhang C., Chen H., Liang Z., et al. Reactive power optimization under interval uncertainty by the linear approximation method and its modified method. IEEE Trans. Smart Grid, 2018, vol. 9, iss. 5, pp. 4587--4600. DOI: https://doi.org/10.1109/TSG.2017.2664816

[3] Bjelogrlic M.R., 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] Xie J., Liang C., Xiao Y. Reactive power optimization for distribution network based on distributed random gradient-free algorithm. Energies, 2018, vol. 11, iss. 3, art. 534. DOI: https://doi.org/10.3390/en11030534

[5] Chen J.-S. Two classes of merit functions for the second-order cone complementarity problem. Math. Meth. Oper. Res., 2006, vol. 64, no. 3, pp. 495--519. DOI: https://doi.org/10.1007/s00186-006-0098-9

[6] 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. 20. DOI: https://doi.org/10.1155/2020/6382507

[7] 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://dx.doi.org/10.12928/telkomnika.v17i5.10767

[8] Packiasudha M., Suja S., Jerome J. A new Cumulative Gravitational Search algorithm for optimal allocation of FACT device to minimize system loss in deregulated electrical power environment. Int. J. Electr. Power Energy Syst., 2017, vol. 84, pp. 34--46. DOI: https://doi.org/10.1016/j.ijepes.2016.04.049

[9] Naderi E., Narimani H., Fathi M., et al. A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Appl. Soft Comput., 2017, vol. 53, pp. 441--456. DOI: https://doi.org/10.1016/j.asoc.2017.01.012

[10] Vishnu M., Sunil K.T.K. An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization. Energies, 2020, vol. 13, art. 2862. DOI: https://doi.org/10.3390/en13112862

[11] Muthukumaran E., Kalyani S. Development of smart controller for demand side management in smart grid using reactive power optimization. Soft Comput., 2021, vol. 25, no. 2, pp. 1581--1594. DOI: https://doi.org/10.1007/s00500-020-05246-3

[12] Ravisekar R., Srinivasan K. Optimal reactive power dispatch with series and shunt facts devices using sine cosine algorithm. IJARET, 2020, vol. 11, iss. 1, pp. 90--109. DOI: https://doi.org/10.34218/ijaret.11.1.2020.012

[13] 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

[14] Maleki A., Rosen M.A. Design of a cost-effective on-grid hybrid wind-hydrogen based CHP system using a modified heuristic approach. Int. J. Hydrogen Energy, 2017, vol. 42, iss. 25, pp. 15973--15989. DOI: https://doi.org/10.1016/j.ijhydene.2017.01.169

[15] Zhang W., Maleki A., Rosen M.A., et al. Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm. Energy Convers. Manag., 2019, vol. 180, pp. 609--621. DOI: https://doi.org/10.1016/j.enconman.2018.08.102

[16] Hatata A., Osman G., Aladl M. An optimization method for sizing a solar/wind/battery hybrid power system based on the artificial immune system. Sustain. Energy Technol. Asses., 2018, vol. 27, pp. 83--93. DOI: https://doi.org/10.1016/j.seta.2018.03.002

[17] IEEE Data Port. IEEE-test systems. Available at: https://ieee-dataport.org/keywords/ieee-test-systems (accessed: 27.04.2022).

[18] 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: https://doi.org/10.19026/rjaset.15.5917

[19] Subbaraj P., Rajnarayan P.N. Optimal reactive power dispatch using self-adaptive real coded Genetic algorithm. Electr. Power Syst. Res., 2009, vol. 79, iss. 2, pp. 374--381. DOI: https://doi.org/10.1016/j.epsr.2008.07.008

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

[21] Reddy S.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

[22] Reddy S.S., Bijwe P.R., Abhyankar A.R. 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