Neural Network Optimal Power Flow(NN-OPF) based on IPSO with Developed Load Cluster Method
An Optimal Power Flow based on Improved Particle
Swarm Optimization (OPF-IPSO) with Generator Capability Curve
Constraint is used by NN-OPF as a reference to get pattern of
generator scheduling. There are three stages in Designing NN-OPF.
The first stage is design of OPF-IPSO with generator capability curve
constraint. The second stage is clustering load to specific range and
calculating its index. The third stage is training NN-OPF using
constructive back propagation method. In training process total load
and load index used as input, and pattern of generator scheduling
used as output. Data used in this paper is power system of Java-Bali.
Software used in this simulation is MATLAB.
[1] Sudhakaran, M., Palanivelu,T.G., "GA and PSO culled hybridtechnique
for economic dispatch problem with prohibited operating zones",
Journal of Zhejiang University, ISSN 1673-565X, pp. 896 - 903, 2007.
[2] Pablo, E., Juan, M.R., "Optimal Power Flow Subject to Security
Constraints Solved With a Particle Swarm Optimizer", IEEE
TransactionsOn Power Systems, Vol. 23, No. 1, pp. 33 - 40, 2008.
[3] Gaing, Z.L., Particle swarm optimization to solving the economic
dispatch considering the generator constrains, IEEE Trans. On Power
System, Vol 18. No. 3, pp. 1187 - 1195, 2003.
[4] Zimmerman,D. Ray, Murilloa E. Carlos, User's Manual A Matlab
Power System Simulation Package, Version 3.2 - September 21,
PSERC, 2007.
[5] Boukir, T., Labdani, R., "Economic power dispatch of power system
with pollution control using multiobjective particle swarm
optimization", University of Sharjah Journal of Pure & Applied
Sciences, Vol.4. No..2, pp. 57 - 73, 2007.
[6] Wang, C.R., Yuan, H.J., "A modified particle swarm optimization
algorithm and its application in optimal power flow problem",
Proceedings of the fourth International Conference on machine
learning and Cybernetics, Guangzhou, 2005.
[7] Balci, H.H, Valenzuela, J.F., "Scheduling electric power generators
using particle swarm optimization combined with the lagrangian
relaxation method", AMCS Appl.Math.Comput.Sci, Vol.14. No. 14, pp.
411 - 421, 2004.
[8] Kumari, M.S., Sydulu, M., "An Improved Evolutionary Computation
Technique for Optimal Power Flow Solution", International Journal of
Innovations in Energy Systems and Power, Vol. 3, no. 1, pp. 32 - 45,
2008.
[9] Younes,M., Rahliga,M., "GA Based Optimal Power Flow Solutions",
Electrical & Instrumentation Engineering Department, Thapar
University, 2008.
[10] Piccolo, A., Vaccaro, A., "Fuzzy Logic Based Optimal Power Flow
Management in Parallel Hybrid Electric Vehicles", Iranian Journal of
Electrical and Computer Engineering, Vol. 4, no. 2, pp. 85 - 93, 2005.
[11] Wong,K.P.,Wong,S.Y.W., "Combined Genetic Algorithm/ Simulated
Annealing /Fuzzy Set to Short Term Generation Scheduling with Takeor
Pay Fuel Contract", IEEE Trans. Power Systems, Vol.11, No.1, pp.
128-136, 1996.
[12] Wong,K.P.,Wong,S.Y.W., "Hybrid Genetic/Simulated Annealing to
Short Term Multiple Fuel-Constrained Generation Scheduling", IEEE
Trans. Power Systems, Vol.12, No.2, pp. 776-784, 1997.
[13] Mat Syai-in, Adi Soeprijanto, T. Hiyama.,"Generator Capability Curve
Constraint for PSO based Optimal Power Flow". International Journal of
Electrical power and Energy Systems Engineering Volume 3.2 .2010 pp
61-66.
[14] Jong Bae Park, etc, An Improved PSO for Economic Dispatch with
Valve-Point Effect, Int, Journal of Innovations in Energy
Ssystems and Power, Vol.1 no.1, Nov. 2006.
[15] Kennedy, J.; Eberhart, R "Particle swarm optimization "
Proceedings., IEEE International Conference on Neural
Networks, Vol4 Page(s): 1942 - 1948 1995.
[16] Gastaldo, P.; Zunino, R.; Vicario, E.; Heynderickx, I" CBP
neural network for objective assessment of image quality "
Proceedings of the International Joint Conference on Neural
Networks,Vol 1 Page(s):194 -199 2003.
[1] Sudhakaran, M., Palanivelu,T.G., "GA and PSO culled hybridtechnique
for economic dispatch problem with prohibited operating zones",
Journal of Zhejiang University, ISSN 1673-565X, pp. 896 - 903, 2007.
[2] Pablo, E., Juan, M.R., "Optimal Power Flow Subject to Security
Constraints Solved With a Particle Swarm Optimizer", IEEE
TransactionsOn Power Systems, Vol. 23, No. 1, pp. 33 - 40, 2008.
[3] Gaing, Z.L., Particle swarm optimization to solving the economic
dispatch considering the generator constrains, IEEE Trans. On Power
System, Vol 18. No. 3, pp. 1187 - 1195, 2003.
[4] Zimmerman,D. Ray, Murilloa E. Carlos, User's Manual A Matlab
Power System Simulation Package, Version 3.2 - September 21,
PSERC, 2007.
[5] Boukir, T., Labdani, R., "Economic power dispatch of power system
with pollution control using multiobjective particle swarm
optimization", University of Sharjah Journal of Pure & Applied
Sciences, Vol.4. No..2, pp. 57 - 73, 2007.
[6] Wang, C.R., Yuan, H.J., "A modified particle swarm optimization
algorithm and its application in optimal power flow problem",
Proceedings of the fourth International Conference on machine
learning and Cybernetics, Guangzhou, 2005.
[7] Balci, H.H, Valenzuela, J.F., "Scheduling electric power generators
using particle swarm optimization combined with the lagrangian
relaxation method", AMCS Appl.Math.Comput.Sci, Vol.14. No. 14, pp.
411 - 421, 2004.
[8] Kumari, M.S., Sydulu, M., "An Improved Evolutionary Computation
Technique for Optimal Power Flow Solution", International Journal of
Innovations in Energy Systems and Power, Vol. 3, no. 1, pp. 32 - 45,
2008.
[9] Younes,M., Rahliga,M., "GA Based Optimal Power Flow Solutions",
Electrical & Instrumentation Engineering Department, Thapar
University, 2008.
[10] Piccolo, A., Vaccaro, A., "Fuzzy Logic Based Optimal Power Flow
Management in Parallel Hybrid Electric Vehicles", Iranian Journal of
Electrical and Computer Engineering, Vol. 4, no. 2, pp. 85 - 93, 2005.
[11] Wong,K.P.,Wong,S.Y.W., "Combined Genetic Algorithm/ Simulated
Annealing /Fuzzy Set to Short Term Generation Scheduling with Takeor
Pay Fuel Contract", IEEE Trans. Power Systems, Vol.11, No.1, pp.
128-136, 1996.
[12] Wong,K.P.,Wong,S.Y.W., "Hybrid Genetic/Simulated Annealing to
Short Term Multiple Fuel-Constrained Generation Scheduling", IEEE
Trans. Power Systems, Vol.12, No.2, pp. 776-784, 1997.
[13] Mat Syai-in, Adi Soeprijanto, T. Hiyama.,"Generator Capability Curve
Constraint for PSO based Optimal Power Flow". International Journal of
Electrical power and Energy Systems Engineering Volume 3.2 .2010 pp
61-66.
[14] Jong Bae Park, etc, An Improved PSO for Economic Dispatch with
Valve-Point Effect, Int, Journal of Innovations in Energy
Ssystems and Power, Vol.1 no.1, Nov. 2006.
[15] Kennedy, J.; Eberhart, R "Particle swarm optimization "
Proceedings., IEEE International Conference on Neural
Networks, Vol4 Page(s): 1942 - 1948 1995.
[16] Gastaldo, P.; Zunino, R.; Vicario, E.; Heynderickx, I" CBP
neural network for objective assessment of image quality "
Proceedings of the International Joint Conference on Neural
Networks,Vol 1 Page(s):194 -199 2003.
@article{"International Journal of Electrical, Electronic and Communication Sciences:53138", author = "Mat Syai'in and Adi Soeprijanto", title = "Neural Network Optimal Power Flow(NN-OPF) based on IPSO with Developed Load Cluster Method", abstract = "An Optimal Power Flow based on Improved Particle
Swarm Optimization (OPF-IPSO) with Generator Capability Curve
Constraint is used by NN-OPF as a reference to get pattern of
generator scheduling. There are three stages in Designing NN-OPF.
The first stage is design of OPF-IPSO with generator capability curve
constraint. The second stage is clustering load to specific range and
calculating its index. The third stage is training NN-OPF using
constructive back propagation method. In training process total load
and load index used as input, and pattern of generator scheduling
used as output. Data used in this paper is power system of Java-Bali.
Software used in this simulation is MATLAB.", keywords = "Optimal Power Flow, Generator Capability Curve,Improved Particle Swarm Optimization, Neural Network", volume = "4", number = "12", pages = "1743-6", }
