Improved Reactive Power Dispatch Using Slime Mould Algorithm

Reevu, Faisal Hossain; Pranto, Samiul Ahsan; Rabby, Rashedul Islam

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dc.contributor.author	Reevu, Faisal Hossain
dc.contributor.author	Pranto, Samiul Ahsan
dc.contributor.author	Rabby, Rashedul Islam
dc.date.accessioned	2022-05-04T06:57:58Z
dc.date.available	2022-05-04T06:57:58Z
dc.date.issued	2021-03-30
dc.identifier.citation	1. K. Ayan, U. Kılıc¸ , Artificial bee colony algorithm solution for optimal reactive power flow, Appl. Soft Comput. 12 (2012) 1477{1482 2. Bhatele, R. P., Sharma, J. D., & Thapar, O. D. (1985). Optimal reactive power control via loss minimization and voltage control. International Journal of Electrical Power & Energy Systems, 7(4), 247-252. 3. Pirnia, M. (2014). Stochastic modeling and analysis of power systems with intermittent energy sources. 4. Eltamaly, A. M., Sayed, Y., El-Sayed, A. H. M., & Elghaffar, A. N. A. (2018). Optimum power flow analysis by Newton raphson method, a case study. Annals of the Faculty of Engineering Hunedoara, 16(4), 51-58. 5. Khan, B., & Singh, P. (2017). Optimal power flow techniques under characterization of conventional and renewable energy sources: A comprehensive analysis. Journal of Engineering, 2017. 6. Dommel, H.W.; Tinney, W.F. Optimal Power flow solutions. IEEE Trans. PAS 1968, 87, 1866–1876. 7. Iyer, S.R.; Ramachandran, K.; Hariharan, S. Optimal reactive power allocation for improved system performance. IEEE Trans. PAS 1984, 103, 1509–1515. 8. Mansour, M.O.; Rehman, T.M.A. Non-linear VAR Optimization using Decomposition and Coordination. IEEE Trans. PAS 1984, 103, 246–255. 9. Granville, S. An Interior Point Method based Optimal Power Flow. IEEE Trans. Power Syst. 1994, 9, 136–146. 10.Wei, H.; Sasaki, H.; Kubokawa, J.; Yokohama, R. An interior point nonlinear programming for optimal power flow problems with a new data structure. IEEE Trans. Power Syst. 1998, 12, 444–877. 11.Momoh, J.A.; Koesslar, R.J.; Bond, M.S.; Stott, B.; Sun, D.; Papalexopoulos, A.; Ristanovic, P. Challenges to optimal power flow. IEEE Trans. Power Syst. 1997, 12, 444–455. 12.Tinney, W.F.; Bright, J.; Damaree, K.; Hughes, B. Some deficiencies in optimal power flows. IEEE Trans. Power Syst. 1987, 3, 164–169. 60 \| P a g e 13.Devaraj, D.; Rosylin, J.P. Genetic Algorithm based Reactive Power Dispatch for Voltage Stability improvement. Int. J. Electr. Power Energy Syst. 2010, 32, 1151–1156. 14.Esmin Ahmed, A.A.; Torres, G.L.; Cao, Y.J. Application of Particle Swarm Optimization to Optimal Power Systems. Int. J. Innov. Comput. Inf. Control 2012, 8, 1705–1716. 15.Varadarajan, M.; Swarup, K.S. Differential Evolutionary Algorithm for Optimal Reactive Power Dispatch. Int. J. Electr. Power Energy Syst. 2008, 30, 435–441. 16.Sujin, P.R.; Prakash, T.R.D.; Linda, M.M. Particle Swarm Optimization Based Reactive Power Optimization. J. Comput. 2010, 2, 73–78. 17.Khanmiri, D.T.; Nasiri, N.; Abedinzadeh, T. Optimal Reactive Power Dispatch using an Improved Genetic Algorithm. Int. J. Comput. Electr. Eng. 2012, 4, 463–466. 18.Wu, Q.H.; Ma, J.T. Power System Optimal Reactive Power Dispatch using Evolutionary Programming. IEEE Trans. Power Syst. 1995, 10, 1243–1249. 19.Lai, L.L.; Ma, J.T. Application of Evolutionary Programming to Reactive Power Planning—Comparison with non-linear programming approach. IEEE/PES Winter Meet. 1996, 96, 248–255. 20.Abido, M.A. Multiobjective Optimal VAR Dispatch using Strength Pareto Evolutionary Algorithm. In Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada, 16–21 July 2006; pp. 730–736. 21.Vaisakh, K.; Rao, P.K. Comparison and Application of Self-Tuning DE/HDE based Algorithms to solve Optimal Reactive Power Dispatch Problems with Multiple Objectives. In Proceedings of the IEEE World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), Coimbatore, India, 9–11 December 2009; pp. 1168–1173. 22.A. Saraswat, A. Saini, Multi-objective optimal reactive power dispatch considering voltage stability in power systems using HFMOEA, Eng. Appl. Artif. Intell.26 (2013) 390–404 23.B. Zhao, et al., A multiagent-based particle swarm optimization approach for optimal reactive power dispatch, IEEE Trans. Power Syst. 20 (2005) 1070– 1078. 61 \| P a g e 24.J.G. Vlachogiannis, K.Y. Lee, A comparative study on particle swarm optimization for optimal steady-state performance of power systems, IEEE Trans. Power Syst. 21 (2006) 1718–1728. 25.Vishnu, M., & TK, S. K. (2020). An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization. Energies, 13(11), 2862. 26.Yenesew, M. (2017). Enhanced particle swarm optimization (PSO) algorithm for reactive power optimization in the distribution system. International Journal of Engineering Research and Modern Education, 2(2), 56-64. 27.Sulaiman, M. H., Mustaffa, Z., Mohamed, M. R., & Aliman, O. (2015). Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Applied Soft Computing, 32, 286-292. 28.Mouassa, S.; Bouktir, T.; Salhi, A. Ant-lion Optimizer for solving Optimal Reactive Power Dispatch problem in power systems. Int. J. Eng. Sci. Technol. 2017, 20, 885–895. [CrossRef] 29.Palappan, A.; Thangavelu, J. A new meta-heuristic Dragon-fly optimization algorithm for Optimal Reactive Power Dispatch Problem. Gazi Univ. J. Sci. 2018, 31, 1107–1121 30.S. Duman, et al., Optimal power flow using gravitational search algorithm, Energy Convers. Manag. 59 (2012) 86–95. 31.B. Shaw, et al., Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm, Int. J. Electr. Power Energy Syst. 55 (2014) 29–40. 32.Niknam, T., Narimani, M. R., Azizipanah-Abarghooee, R., & BahmaniFirouzi, B. (2013). Multiobjective optimal reactive power dispatch and voltage control: a new opposition-based self-adaptive modified gravitational search algorithm. IEEE Systems journal, 7(4), 742-753. 62 \| P a g e 33.A.A. El Ela, M. Abido, S. Spea, Differential evolution algorithm for optimal reactive power dispatch, Electric Power Systems Research 81 (2) (2011) 458–464. 34.Rao, R.V. Jaya: A simple and new optimization algorithm for solving constrained and un-constrained optimization problems. Int. J. Ind. Eng. Comput. 2016, 7, 19–34. 35.Kessler, D. (1982). Plasmodial structure and motility. Cell biology of Physarum and Didymium/edited by Henry C. Aldrich, John W. Daniel. 36.Kamiya, N. (1940). The control of protoplasmic streaming. Science, 92(2394), 462-463. 37.Nakagaki, T., Yamada, H., & Ueda, T. (2000). Interaction between cell shape and contraction pattern in the Physarum plasmodium. Biophysical chemistry, 84(3), 195-204. 38.Christie, R. (2000). Power systems test case archive. Electrical Engineering dept., University of Washington, 108. 39.Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systesms, 111, 300-323. 40.Samir, H. A. M. A. N. I. (2017). Ajustement Optimal des paramètres du filtre de Kalman étendu en vue d'estimation d'état d'une machine synchrone à aimants permanents (Doctoral dissertation, UNIVERSITE MOHAMED BOUDIAF-M’SILA)	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1470
dc.description	Supervised by Dr. Ashik Ahmed Professor, Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Boardbazar, Gazipur-1704	en_US
dc.description.abstract	Optimal Reactive Power Dispatch (ORPD) problem is a genuine concern for any power system. It is important to determine the ideal reactive power dispatch for different kinds of load conditions. ORPD is responsible for reducing the active power loss in a system by adjusting the reactive power control variables and consequently influences the economics and net efficiency of the power System. The optimization of reactive power also ensures the voltage stability and thus maintains the security and reliability of the system. Slime Mould Algorithm (SMA) is a novel Metaheuristic Algorithm (MA) which replicates the behaviour of slime mould for searching and collecting food with the help of the excellent exploratory capabilities of slime mould. This paper brings forth the feasibility of the application of SMA to the realm of optimal reactive power flow. In this thesis, IEEE 30-bus test method is used to show the feasibility of this method. The findings were analyzed and compared to other approaches that are used for solving ORPD problems. SMA is a more efficient and robust system even compared to the most recent swarm intelligence based metaheuristic algorithms and presents a possibility for unparalleled efficiency	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Improved Reactive Power Dispatch Using Slime Mould Algorithm	en_US
dc.type	Thesis	en_US