DocumentCode :
3146961
Title :
Application of artificial neural networks to unit commitment
Author :
Sendaula, Musoke H. ; Biswas, Saroj K. ; Eltom, Ahmed ; Parten, Cliff ; Kazibwe, Wilson
Author_Institution :
Temple Univ., Philadelphia, PA, USA
fYear :
1991
fDate :
23-26 Jul 1991
Firstpage :
256
Lastpage :
260
Abstract :
Artificial neural networks are currently being applied to a variety of complex combinatorial optimization and nonlinear programming problems. In this paper, a combination of Hopfield Tank type, and Chua-Lin type artificial neural networks is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various constraints in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the artificial neural network. The novel feature of the proposed approach is that the nonlinear programming and the combinatorial optimization problems are solved simultaneously by one network. An illustrative example is also presented
Keywords :
Hopfield neural nets; Lyapunov methods; combinatorial mathematics; load dispatching; load distribution; nonlinear programming; power engineering computing; power systems; Chua-Lin type; Hopfield Tank type; Lyapunov function; artificial neural networks; combinatorial optimization; generalized energy function; load dispatching; load distribution; network dynamics; nonlinear programming; power engineering computing; power systems; unit commitment; Artificial neural networks; Circuits; Cost function; Dynamic programming; Functional programming; Lyapunov method; Neural networks; Power generation economics; Power system dynamics; Power system economics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
Type :
conf
DOI :
10.1109/ANN.1991.213467
Filename :
213467
Link To Document :
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