Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

This paper proposes Embellished Particle Swarm Optimization (EPSO) algorithm for solving reactive power problem .The main concept of Embellished Particle Swarm Optimization is to extend the single population PSO to the interacting multi-swarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be upheld. Concurrently, the swarm-to-swarm mechanism drastically speeds up the swarm community to converge to the global near optimum. In order to evaluate the performance of the proposed algorithm, it has been tested in standard IEEE 57,118 bus systems and results show that Embellished Particle Swarm Optimization (EPSO) is more efficient in reducing the Real power losses when compared to other standard reported algorithms.

[1]  Cristian Bovo,et al.  A GA approach to compare ORPF objective functions including Secondary Voltage Regulation , 2012 .

[2]  A. Monticelli,et al.  Security-Constrained Optimal Power Flow with Post-Contingency Corrective Rescheduling , 1987, IEEE Transactions on Power Systems.

[3]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[4]  S. M. Shahidehpour,et al.  Linear reactive power optimization in a large power network using the decomposition approach , 1990 .

[5]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[6]  Jing Liu,et al.  Quantum-behaved particle swarm optimization with mutation operator , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[7]  Chaohua Dai,et al.  Seeker Optimization Algorithm for Optimal Reactive Power Dispatch , 2009, IEEE Transactions on Power Systems.

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[10]  Osvaldo R. Saavedra,et al.  Optimal reactive power dispatch using evolutionary computation: extended algorithms , 1999 .

[11]  Leandro dos Santos Coelho,et al.  PSO-E: Particle Swarm with Exponential Distribution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  K. Lee,et al.  A United Approach to Optimal Real and Reactive Power Dispatch , 1985, IEEE Transactions on Power Apparatus and Systems.

[13]  Renato A. Krohling,et al.  Gaussian particle swarm with jumps , 2005, 2005 IEEE Congress on Evolutionary Computation.

[14]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[15]  Fuli Wang,et al.  An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow , 2014 .

[16]  Kwang Y. Lee,et al.  Optimal Real and Reactive Power Control Using Linear Programming , 1993 .

[17]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[18]  Eric Hcbson,et al.  Network Constrained Reactive Power Control Using Linear Programming , 1980, IEEE Transactions on Power Apparatus and Systems.

[19]  A.C.Z. de Souza,et al.  Comparison of performance indices for detection of proximity to voltage collapse , 1996 .

[20]  Kwang Y. Lee,et al.  Fuel-cost minimisation for both real-and reactive-power dispatches , 1984 .

[21]  B. Yegnanarayana,et al.  Genetic-algorithm-based optimal power flow for security enhancement , 2005 .