AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT
Keywords:Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Optimal Reactive Power Dispatch
In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum. In order to assess the efficiency of proposed algorithm, it has been tested on Standard IEEE 30 system and compared to other standard algorithms. The simulation results demonstrate worthy performance of the Amended Particle Swarm Optimization Algorithm (APSOA) in solving optimal reactive power dispatch problem.
M. A. Abido, J. M. Bakhashwain, “A novel multi objective evolutionary algorithm for optimal reactive power dispatch problem,” in proc. Electronics, Circuits and Systems conf., vol. 3, pp. 1054-1057, 2003.
W. N. W. Abdullah, H. Saibon, A. A. M. Zain, K. L. Lo, “Genetic Algorithm for Optimal Reactive Power Dispatch,” in proc. Energy Management and Power Delivery conf., vol. 1, pp. 160-164, 1998.
K. Y. Lee, Y. M. Park, J. L. Ortiz, “Fuel-cost minimisation for both real and reactive-power dispatches,” in proc. Generation, Transmission and Distribution conf., vol. 131, pp. 85-93, 1984. DOI: https://doi.org/10.1049/ip-c.1984.0012
S. Granville, “Optimal Reactive Dispatch Trough Interior Point Methods,” IEEE Trans. on Power Systems, vol. 9, pp. 136-146, 1994. DOI: https://doi.org/10.1109/59.317548
N. I. Deeb, S. M. Shahidehpour, “An Efficient Technique for Reactive Power Dispatch Using a Revised Linear Programming Approach,” Electric Power System Research, vol. 15, pp. 121-134, 1988. DOI: https://doi.org/10.1016/0378-7796(88)90016-8
N. Grudinin, “Reactive Power Optimization Using Successive Quadratic Programming Method,” IEEE Trans. on Power Systems, vol. 13, pp. 1219-1225, 1998.
M. A. Abido, “Optimal Power Flow Using Particle Swarm Optimization,” Electrical Power and Energy Systems, vol. 24, pp. 563-571, 2002. DOI: https://doi.org/10.1016/S0142-0615(01)00067-9
A. Abou El Ela, M. A. Abido, S. R. Spea, “Differential Evolution Algorithm for Optimal Reactive Power Dispatch,” Electric Power Systems Research, vol. 81, pp. 458-464, 2011. DOI: https://doi.org/10.1016/j.epsr.2010.10.005
V. Miranda, N. Fonseca, “EPSO-Evolutionary Particle Swarm Optimization, A New Algorithm with Applications in Power Systems,” in Proc. of Transmission and Distribution conf., vol. 2, pp. 745-750,2002.
C.A. Canizares, A.C.Z.de Souza and V.H. Quintana, “Comparison of performance indices for detection of proximity to voltage collapse,’’ vol. 11. no.3, pp.1441-1450, Aug 1996.
E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, “GSA: A gravitational search algorithm,” Information Sciences, vol. 179, pp. 2232-2248, 2009.
X. Lai and M. Zhang, "An efficient ensemble of GA and PSO for real function optimization," in 2nd IEEE International Conference on Computer Science and Information Technology, 2009, pp. 651-655.
A. A. A. Esmin, G. Lambert-Torres, and G. B. Alvarenga, "Hybrid Evolutionary Algorithm Based on PSO and GA mutation," in proceeding of the Sixth International Conference on Hybrid Intelligent Systems (HIS 06), 2007, p. 57. DOI: https://doi.org/10.1109/HIS.2006.264940
L. Li, B. Xue, B. Niu, L. Tan, and J. Wang, "A Novel PSO-DE-Based Hybrid Algorithm for Global Optimization," in Lecture Notes in Computer Science.: Springer Berlin / Heidelberg, 2008, pp. 785-793.
N. Holden and AA Freitas, "A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining," Journal of Artificial Evolution and Applications (JAEA), 2008. DOI: https://doi.org/10.1155/2008/316145
S. Durairaj, P. S. Kannan, D. Devaraj, “Application of Genetic Algorithm to Optimal Reactive Power Dispatch Including Voltage Stability Constraint,” Journal of Energy & Environment, vol. 4, pp. 63-73, 2005.
J. Kennedy and RC. Eberhart, "Particle swarm optimization," in Proceedings of IEEE international conference on neural networks, vol. 4, 1995, pp. 1942–1948.
Y. Shi and R.C. Eberhart, "A modified Particle Swarm Optimiser," inIEEE International Conference on Evolutionary Computation,Anchorage, Alaska, 1998.
E. Rashedi, S. Nezamabadi, and S. Saryazdi, "GSA: A Gravitational Search Algorithm,"Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
Wu Q H, Ma J T. “Power system optimal reactive power dispatch using evolutionary programming”, IEEE Transactions on power systems 1995; 10(3): 1243-1248. DOI: https://doi.org/10.1109/59.466531
S.Durairaj, D.Devaraj, P.S.Kannan ,“Genetic algorithm applications to optimal reactive power dispatch with voltage stability enhancement”, IE(I) Journal-EL Vol 87,September 2006.
D.Devaraj, “Improved genetic algorithm for multi – objective reactive power dispatch problem”, European Transactions on electrical power 2007; 17: 569-581. DOI: https://doi.org/10.1002/etep.146
P. Aruna Jeyanthy and Dr. D. Devaraj “Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm”, International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, August 2010 1793-8163. DOI: https://doi.org/10.7763/IJCEE.2010.V2.220
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