Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30174
Transmission Lines Loading Enhancement Using ADPSO Approach

Authors: M. Mahdavi, H. Monsef, A. Bagheri

Abstract:

Discrete particle swarm optimization (DPSO) is a powerful stochastic evolutionary algorithm that is used to solve the large-scale, discrete and nonlinear optimization problems. However, it has been observed that standard DPSO algorithm has premature convergence when solving a complex optimization problem like transmission expansion planning (TEP). To resolve this problem an advanced discrete particle swarm optimization (ADPSO) is proposed in this paper. The simulation result shows that optimization of lines loading in transmission expansion planning with ADPSO is better than DPSO from precision view point.

Keywords: ADPSO, TEP problem, Lines loading optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073363

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1212

References:


[1] J. Kennedy and R. Eberhart, Particle swarm optimization, IEEE International Conference on Neural Networks, Vol. 4, 1995, pp. 1942- 1948.
[2] Y. X. Jin, H.Z. Cheng, J. Y. Yan, and L. Zhang, New discrete method for particle swarm optimization and its application in transmission network expansion planning, Electric Power Systems Research, Vol. 77, No. 3-4, 2007, pp. 227-233.
[3] H. Shayeghi, A. Jalili, and H. A. Shayanfar, Multi-stage fuzzy load frequency control using PSO, Energy Conversion and Management, Vol. 49, No. 10, 2008, pp. 2570-2580.
[4] J. Kennedy, R. Eberhart, Y. Shi. Swarm intelligence, Morgan Kaufmann Publishers, San Francisco, 2001.
[5] A. R. Abdelaziz, Genetic algorithm-based power transmission expansion planning, Proc. the 7th IEEE International Conference on Electronics, Circuits and Systems, Jounieh, Vol. 2, December 2000, pp. 642-645.
[6] S. Binato, G. C. de Oliveira, J. L. Araujo, A greedy randomized adaptive search procedure for transmission expansion planning, IEEE Trans. Power Systems, Vol. 16, No. 2, 2001, pp. 247-253.
[7] S. Binato, M. V. F. Periera, S. Granville, A new Benders decomposition approach to solve power transmission network design problems, IEEE Trans. Power Systems, Vol. 16, No. 2, 2001, pp. 235-240.
[8] R. Romero, A. Monticelli, A hierarchical decomposition approach for transmission network expansion planning, IEEE Trans. Power Systems, Vol. 9, No. 1, 1994, pp. 373-380.
[9] M. V. F. Periera, L. M. V. G. Pinto, Application of sensitivity analysis of load supplying capacity to interactive transmission expansion planning, IEEE Trans. Power Apparatus and Systems, Vol. PAS-104, 1985, pp. 381-389.
[10] R. A. Gallego, A. Monticelli, R. Romero, Transmission system expansion planning by an extended genetic algorithm, IEE Proc. Generation, Transmission and Distribution, Vol. 145, No. 3, 1998, pp. 329-335.
[11] E. L. da Silva, H. A. Gil, J. M. Areiza, Transmission network expansion planning under an improved genetic algorithm, IEEE Trans. Power Systems, Vol. 15, No. 3, 2000, pp. 1168-1174.
[12] H. Shayeghi, S. Jalilzadeh, M. Mahdavi, H. Haddadian, Studying influence of two effective parameters on network losses in transmission expansion planning using DCGA, Energy Conversion and Management, Vol. 49, No. 11, 2008, pp. 3017-3024.
[13] M. Mahdavi, H. Shayeghi, A. Kazemi, DCGA based evaluating role of bundle lines in TNEP considering expansion of substations from voltage level point of view, Energy Conversion and Management, Vol. 50, No. 8, 2009, pp. 2067-2073.
[14] R. Romero, R. A. Gallego, A. Monticelli, Transmission system expansion planning by simulated annealing, IEEE Trans. Power Systems, Vol. 11, No. 1, 1996, pp. 364-369.
[15] R. A. Gallego, A. B. Alves, A. Monticelli, R. Romero, Parallel simulated annealing applied to long term transmission network expansion planning, IEEE Trans. Power Systems, Vol. 12, No. 1, 1997, pp. 181- 188.
[16] R. A. Gallego, R. Romero, A. J. Monticelli, Tabu search algorithm for network synthesis, IEEE Trans. Power Systems, Vol. 15, No. 2, 2000, pp. 490-495.
[17] H. Shayeghi, M. Mahdavi, A. Kazemi, Discrete particle swarm optimization algorithm used for TNEP considering network adequacy restriction, International Journal of Electrical, Computer, and Systems Engineering, Vol. 3, No. 1, 2009, pp. 8-15.
[18] Z. S. Lu, Z. R. Hou, Particle swarm optimization with adaptivemutation, Acta Electronica Sinica, Vol. 32, No.3, 2004, pp. 416-420.
[19] M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evolutionary Computation, Vol. 6, No. 1, 2002, pp. 58-73.
[20] A. Jalilvand, A. Kimiyaghalam, A. Ashouri, M. Mahdavi, Advanced particle swarm optimization-based PID controller parameters tuning, The 12th IEEE International Multitopic Conference, Pakistan, 2008, pp. 429-435.