Title :
A hybrid Hopfield neural network-quadratic programming approach for dynamic economic dispatch problem
Author :
Abdelaziz, Almoataz Y. ; Mekhamer, Said F. ; Kamh, Mohamed Z. ; Badr, Mohamed A L
Author_Institution :
Electr. Power & Machines Dept., Ain Shams Univ., Cairo
Abstract :
This paper introduces a solution of the dynamic economic dispatch (DED) problem using a hybrid approach of Hopfield neural network (HNN) and quadratic programming (QP). The hybrid algorithm is based on using enhanced HNN; to solve the static part of the problem; and the QP algorithm for solving the dynamic part of the DED. This technique guarantees the global optimality of the solution due to its look-ahead capability. The new algorithm is applied and tested to an example from the literature and the solution is then compared with that obtained by some other techniques to prove the superiority and effectiveness of the proposed algorithm.
Keywords :
Hopfield neural nets; dynamic programming; power engineering computing; power system economics; HNN; QP algorithm; dynamic economic dispatch problem; hybrid Hopfield neural network-quadratic programming approach; look-ahead capability; Artificial intelligence; Dynamic programming; Economic forecasting; Hopfield neural networks; Job shop scheduling; Power engineering and energy; Power generation economics; Power system economics; Quadratic programming; Testing; Dynamic economic dispatch (DED); Hopfield neural network (HNN); look ahead capability; ramp rates;
Conference_Titel :
Power System Conference, 2008. MEPCON 2008. 12th International Middle-East
Conference_Location :
Aswan
Print_ISBN :
978-1-4244-1933-3
Electronic_ISBN :
978-1-4244-1934-0
DOI :
10.1109/MEPCON.2008.4562378