DocumentCode :
3239740
Title :
Optimal active power flow solutions using a modified Hopfield neural network
Author :
Hartati, Rukmi Sari ; El-Hawary, M.E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
Volume :
1
fYear :
2001
fDate :
2001
Firstpage :
189
Abstract :
The optimal power flow is a general nonlinear programming problem with a nonlinear objective function and nonlinear functional equality and inequality constraints. This paper presents a proposed strategy for optimal active power flow using a modified Hopfield neural network. The objective function is the incremental generation cost function in quadratic form which is expanded in a second-order Taylor series. The equality and inequality constraints are modelled using a linearized network and appended to the objective function using suitable penalty functions to form an augmented cost function. The Hopfield neural network was simulated on a digital computer for fourteen-bus and thirty-bus test system. The optimal solution obtained using this approach is comparable to the solution obtained using the conventional method
Keywords :
Hopfield neural nets; digital simulation; load flow; nonlinear programming; power generation economics; power system simulation; series (mathematics); augmented cost function; digital computer; fourteen-bus test system; incremental generation cost function; linearized network; modified Hopfield neural network; nonlinear functional equality; nonlinear functional inequality; nonlinear objective function; nonlinear programming problem; objective function; optimal active power flow solutions; penalty functions; second-order Taylor series; thirty-bus test system; Computational modeling; Computer networks; Computer simulation; Cost function; Functional programming; Hopfield neural networks; Load flow; Power system modeling; System testing; Taylor series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 2001. Canadian Conference on
Conference_Location :
Toronto, Ont.
ISSN :
0840-7789
Print_ISBN :
0-7803-6715-4
Type :
conf
DOI :
10.1109/CCECE.2001.933681
Filename :
933681
Link To Document :
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