Title :
An efficient neural approach to economic load dispatch in power systems
Author :
da Silva, I.N. ; Nepomuceno, L.
Author_Institution :
Dept. of Electr. Eng., State Univ. of Sao Paulo, Brazil
Abstract :
A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper. Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements The ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization The neural networks applied in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points The internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible equilibrium points. A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.
Keywords :
load dispatching; neural nets; optimisation; power system analysis computing; power system economics; Hopfield model; artificial neural networks; complex nonlinear function; economic load dispatch; feasible equilibrium points; high computational rates; internal parameters; network convergence guarantee; nonlinear optimization; processing elements; valid-subspace technique; Artificial neural networks; Computer networks; Cost function; Hopfield neural networks; Neural networks; Power generation; Power generation economics; Power system economics; Power system modeling; Power systems;
Conference_Titel :
Power Engineering Society Summer Meeting, 2001
Conference_Location :
Vancouver, BC, Canada
Print_ISBN :
0-7803-7173-9
DOI :
10.1109/PESS.2001.970255