Title :
Application of neural networks to unit commitment
Author :
Yalcinoz, T. ; Short, M.J. ; Cory, B.J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Nigde Univ., Turkey
fDate :
6/21/1905 12:00:00 AM
Abstract :
In this paper, an improved Hopfield neural network which was described previously by the authors (1997) is applied to the power system unit commitment problem. A new mapping process has been used and a computational method for obtaining the weights and biases is described using a slack variable technique for handling inequality constraints
Keywords :
Hopfield neural nets; power generation dispatch; power generation planning; power generation scheduling; power system analysis computing; Hopfield neural network; biases; computational method; inequality constraints handling; mapping process; power systems; slack variable technique; unit commitment; weights; Artificial neural networks; Costs; Hopfield neural networks; Lagrangian functions; Neural networks; Power engineering computing; Power generation economics; Propagation losses; Relaxation methods; Spinning;
Conference_Titel :
Africon, 1999 IEEE
Conference_Location :
Cape Town
Print_ISBN :
0-7803-5546-6
DOI :
10.1109/AFRCON.1999.821841
