A PSO based Artificial Neural Network approach for short term unit commitment problem
Keywords: unit commitment, artificial neural network, dynamic programming, particle swarm optimization, swarm intelligence.
Mehran University Research Journal of Engineering & Technology
Volume 29 , Issue 4
AFTAB AHMAD , AZZAM UL ASAR AHMAD ,
References
1. 
A.J. Wood and B.F. Wollenberg. , Power generation, operation & control, John Wiley & Sons, Inc, NY (1984). 
2. 
Xiaomin, B., and Shaidehpour, S.M. ‘Extended neighbourhood search algorithm for constrained unit commitment’, Electric Power Energy System, 1997, 19, (5), pp. 349–356 
3. 
Sheble, G.B., and Maifeld, T.T. "Unit commitment by genetic algorithm and expert system" Electric Power System Research, 1994, 30, pp. 115121 
4. 
Yamin, H.Y. "Review on methods of generation scheduling in electric power systems" Electric Power System Research., 2004, 69, pp. 227248 
5. 
Bakirtzis, A.G., and Zoumas, C.E.: "Lambda of Lagrangian relaxation solution to unit commitment problem" IEE Proceedings on Generation Transmission and Distribution, 2000, 147, pp. 131136 
6. 
Ma,X., ElKeib, A.A., Smith, R.E., and Ma, H. "A genetic algorithm based approach to thermal unit commitment of electric power systems" Electric Power System Research, 1995, 34, pp. 2936 
7. 
Orero, S.O., and Irving, M.R. "A genetic algorithm for generator scheduling in power systems" Electric Power Energy Systems, 1996, 18, (1), pp. 1926 
8. 
Kazarlis, S.A., Bakirtzis, A.G., and Petridis, V. "A genetic algorithm solution to the unit commitment problem" IEEE Transactions on Power Systems, Vol. 11, No.1, February 1996, pp. 8392. 
9. 
Mantawy, A.H., AbdelMagid, Y.L., and Selim, S.Z.: ‘Unit commitment by tabu search’, IEE Proceedings on Generation Transmission and Distribution, 1998, 145,(1), pp. 56–64 
10. 
Purushothama, G.K., Narendranath, U.A., and Jenkins, L.: ‘Unit commitment using a stochastic extended neighbour hood search’, IEE Proc., Gener. Transm. Distrib., 2003, 150, (1), pp. 67–72 
11. 
Ongsakul, W.P., and Nit, P. "Unit commitment by enhanced adaptive Lagrangian relaxation" IEEE Transactions, 2004, PS19, (1), pp. 620628 
12. 
Rajan, C.C.A., Mohan, M.R., and Manivannan, K. "Neural based tabu search method for solving unit commitment problem" IEE Proc., Gener. Transm. Distrib., 2003, 150, (4), pp. 469474 
13. 
Ouyang Z, Shahidehpour SM., "A hybrid artificial neural networkdynamic programming approach to unit Commitment" IEEE Transactions on Power System 1992;7(2):23642. 
14. 
Sasaki H, Fujii Y, Watanbe M, Kubokawa J, Yorino N. , "A solution method of unit commitment by artificial neural networks" IEEE Transactions on Power Systems, Vol. 7, No. 3, August 1992 
15. 
Sasaki H, Fujii Y, Watanbe M, Kubokawa J, Yorino N. , "A solution method using neural network for the Generator commitment problem" Electrical Eng in Japan 1992;112:5561. 
16. 
T. Yalcinoz, M. J. Short and B.J. Cory, "Application of neural networks to unit commitment" IEEE Transactions on Power System 1999, pp. 649654. 
17. 
M.H. Wong, T.S. Chung, Y.K. Wong, "An evolving neural network approach in unit commitment solution" Microprocessors and Microsystems 24 (2000) pp. 251262. 
18. 
Z. Ouyang, S. M. Shahidehpour, "A multiStage intelligent system for Unit Commitment" IEEE Transactions on Power Systems, Vol. 7, No. 2, May 1992. 
19. 
ShyhJier Huang, ChingLien Huang, "Application of Genetic based Neural Networks to thermal Unit commitment" IEEE Transactions on Power Systems, Vol. 12, No. 2, May 1997 
20. 
Inversion Grimaldi E. M., Grimaccia, Mussetta, F. M., and Zich, R E. "PSO as an Effective Learning Algorithm for Neural Network Applications", Proceedings International Conference on Computational Electromagnetic and Its Applications, 2004. 
21. 
Kennedy, J., and Eberhart, R. C., "Particle Swarm Optimization", Proceedings of the IEEE International Conference on Neural Networks, Volume IV, pages 19421948. Perth, Australia, 1995. 
22. 
Eberhart, R. C. and Kennedy J, "A New Optimizer using Particle Swarm Theory", Proceedings of the Sixth International Symposium on Micro Machine and Human Science, page 3943, Nagoya, Japan, 1995. 
23. 
Gudise V. G., and Venayagamoorthy, G. K., "Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks", Proceedings of the IEEE Swarm Intelligence symposium, 2003. 
24. 
Shi, Y. H., Eberhart, R. C., "A Modified Particle Swarm Optimizer", IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 1998. 
25. 
Shi, Y. H., Eberhart, R. C., "Parameter Selection in Particle Swarm Optimization", The 7th Annual Conference on Evolutionary Programming, San Diego, USA, 1998. 
26. 
Maurice Clerc, "The Swarm and the Queen, Towards a Deterministic and Adaptive Particle Swarm Optimization", Proceedings of Congress on Evolutionary computation, Washington DC, pp 19511957, 1999. 
27. 
Bergh, F. van den, "An Analysis of Particle Swarm Optimizers," Ph.D. Dissertation, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002 
28. 
Yamille del Valle, et. al., "Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems"IEEE Transactions on Evolutionary Computation, Vol. 12, NO. 2, APRIL 2008 pp.171195. 
29. 
S. Mohagheghi, Y. Del Valle, G. Venayagamoorthy, and R. Harley, "A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM" IEEE Proceedings in Swarm Intelligence Symposium, June 2005, pp. 381384 
30. 
G. Gudise and G. Venayagamoorthy, "Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks," IEEE Proceedings in Swarm Intelligence Symposium April 2003, pp. 110117. 
31. 
J. ChiaFeng, "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design," IEEE Trans. Syst., Man, Cybern., Part B: Cybern., vol. 34, no. 2, pp. 9971006, Apr. 2004. 
32. 
Simi P. Valsan, K. S. Swarup, "Hopfield neural network approach to the solution of economic dispatch and unit commitment" Proceedings of International conference on Intelligent sensing and Information processing, 2004, pages 311316. 
33. 
K.S. Swarup, S. Yamashiro, "A Genetic Algorithm Approach to the Solution of the Basic Unit Commitment Problem", IEE Japan national Convention Conference, PP. 2934, October 1997. 


