Security-constrained optimal rescheduling of real power using Hopfield neural network

Author

Ghosh, Soumen ; Chowdhury, Badrd H.

Author_Institution

Dept. of Electr. Eng., Wyoming Univ., Laramie, WY, USA

Volume

11

Issue

4

fYear

1996

fDate

11/1/1996 12:00:00 AM

Firstpage

1743

Lastpage

1748

Abstract

A new method for security-constrained corrective rescheduling of real power using the Hopfield neural network is presented. The proposed method is based on solution of a set of differential equations obtained from transformation of an energy function. Results from this work are compared with the results from a method based on dual linear programming formulation of the optimal corrective rescheduling. The minimum deviations in real power generations and loads at buses are combined to form the objective function for optimization. Inclusion of inequality constraints on active line flow limits and equality constraint on real power generation load balance assures a solution representing a secure system. Transmission losses are also taken into account in the constraint function

Keywords

Hopfield neural nets; differential equations; linear programming; load dispatching; power system analysis computing; power system security; scheduling; Hopfield neural network; active line flow limits; corrective rescheduling; differential equations; dual linear programming formulation; energy function transformation; equality constraint; inequality constraints; optimal corrective rescheduling; real power; real power generation; real power generation load balance; security-constrained optimal rescheduling; transmission losses; Analog computers; Computer networks; Constraint optimization; Differential equations; Hopfield neural networks; Linear programming; Mathematical model; Power generation dispatch; Power generation economics; Propagation losses;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.544637

Filename

544637