Title :
Enhancing the performance of Hopfield Neural Network applied to the Economic Dispatch Problem
Author :
Mekhamer, S.F. ; Abdelaziz, A.Y. ; Badr, M.A.L. ; Kamh, M.Z.
Author_Institution :
Dept. of Elec. Power & Machines, Ain-Shams Univ., Cairo, Egypt
Abstract :
This paper introduces some modifications to the conventional Hopfield Neural Network (HNN) to enhance its performance. A comprehensive study of the effect of the HNN parameters on the solution quality of the Economic Dispatch Problem (EDP), as a case study, is done. By investigating the describing curves, the best values for the HNN parameters are tuned. To further improve the solution quality, an adaptive correction factor is proposed and introduced to the EDP solution obtained by HNN. To investigate the effect of the modifications on the solution quality of the EDP, two case studies are selected and solved. Comparisons of results are then made with others to prove the validity and effectiveness of the proposed modifications.
Keywords :
Hopfield neural nets; load dispatching; power engineering computing; power system economics; EDP solution quality; HNN parameters; Hopfield Neural Network; adaptive correction factor; economic dispatch problem; Cost function; Fuel economy; Gradient methods; Hopfield neural networks; Mathematical model; Modems; Power generation economics; Power system economics; Power systems; Propagation losses; Economic dispatch problem; Hopfield Neural Network; adaptive correction factor; energy function; weighting factor;
Conference_Titel :
Power Systems Conference, 2006. MEPCON 2006. Eleventh International Middle East
Conference_Location :
El-Minia
Print_ISBN :
978-1-4244-5111-1