Transmission Lines Loading Enhancement Using ADPSO Approach
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:58818", author = "M. Mahdavi and H. Monsef and A. Bagheri", title = "Transmission Lines Loading Enhancement Using ADPSO Approach", 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.", volume = "4", number = "3", pages = "516-6", }