An efficient Hopfield network to solve economic dispatch problems with transmission system representation

International Journal of Electrical Power and Energy System

Volumn 26, Issue 9, 2004, Pages 733-738

  Da Silva, Ivan N ^a   Nepomuceno, Leonardo ^a   Bastos, Thiago M ^a  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

Artificial neural networks; Economic dispatch; Hopfield model

Indexed keywords

COMPUTER SIMULATION; ELECTRIC INDUSTRY; ELECTRIC LOADS; ELECTRIC POWER SYSTEMS; INDUSTRIAL ECONOMICS; LINEAR EQUATIONS; MATHEMATICAL MODELS; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

ACTIVE POWER FLOW; ECONOMIC DISPATCH; HOPFIELD MODELS; LOAD FLOW EQUATIONS;

ELECTRIC POWER TRANSMISSION;

EID: 4043104078     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2004.05.007     Document Type: Article

References (21)

1. Chowdhury BH, Rahman S. A review of recent advances in economic dispatch. IEEE Trans Power Syst 1990;5(4):1248-57.
2. Happ HH. Optimal power dispatch - a comprehensive survey. IEEE Trans Power Appar Syst 1977;PAS-96(3):841-54.
3. Silva IN, Arruda LVR, Amaral WC. A modified Hopfield architecture for solving non-linear programming problems with bounded variables. Neural Netw World 2000;10(4):713-22.
4. Walsh MP, O'Malley MJ. Augmented Hopfield network for unit commitment and economic dispatch. IEEE Trans Power Syst 1997; 12(4):1765-74.
5. Su CT, Chiou GJ. A fast-computation Hopfield method to economic dispatch of power systems. IEEE Trans Power Syst 1997;12(4): 1759-64.
6. Park JH, Kim YS, Eom IK, Lee KY. Economic load dispatch for piecewise quadratic cost function using Hopfield neural network. IEEE Trans Power Syst 1993;8(3):1030-8.
7. Jiang A, Ertem S. Economic Dispatch with non-monotonicaly increasing incremental cost units and transmission system losses. IEEE Trans Power Syst 1995;10(2):891-7.
8. Hopfield JJ. Neurons with a graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci 1984;81:3088-92.
9. Haykin S. Neural networks. New York: Prentice-Hall; 1999.
10. Aiyer SVB, Niranjan M, Fallside F. A theoretical investigation into the performance of the Hopfield model. IEEE Trans Neural Netw 1990;1:53-60.
11. Silva IN, Arruda LVR, Amaral WC. Robust estimation of parametric membership regions using artificial neural networks. Int J Syst Sci 1997;28(5):447-55.
12. Abe S. Global convergence and suppression of spurious states of the Hopfield neural networks. IEEE Trans Circuits Syst 1993;CAS-40: 246-57.
13. Bazaraa MS, Jarvis JJ. Linear programming and network flows. New York: Wiley; 1977.
14. Wong KP, Doan K. A recursive economic dispatch algorithm for assessing the costs of thermal generator schedules. IEEE Trans Power Syst 1992;7(2):577-83.
15. Jiang A, Ertem S. Economic dispatch with non-monotonically increasing incremental cost units and transmission system losses. IEEE Trans Power Syst 1995;10:891-7.
16. Silva IN, Nepomuceno L. An efficient neural approach to economic load dispatch in power systems Proceedings of the IEEE PES Summer Meeting, CD-Rom 2001.
17. Su CT, Chiou GJ. An enhanced Hopfield model for economic dispatch considering prohibited zones. Electric Power Syst Res 1997;42:71-6.
18. Yalcinoz T, Short MJ. Neural networks approach for solving economic dispatch problem with transmission capacity constraints. IEEE Trans Power Syst 1998;13:307-13.
19. Yalcinoz T, Cory BJ, Short MJ. Hopfield neural network approaches to economic dispatch problems. Electrical Power Energy Syst 2001; 23:435-42.
20. Luenberger DG. Linear and non-linear programming. Reading, MA: Addison-Wesley; 1989.
21. Silva IN, Nepomuceno L, Bastos TM. Designing a modified Hopfield network to solve an economic dispatch problem with non-linear cost function. Proceedings of the IEEE World Congress on Computational Intelligence, May 2002 p. 1160-65.