Optimal active power flow solutions using a modified Hopfield neural network

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 i...

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Bibliographic Details
Main Authors: Hartati, R.S., El-Hawary, M.E.
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer networks
Computer simulation
Cost function
Functional programming
Hopfield neural networks
Load flow
Power system modeling
System testing
Taylor series
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Summary: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.
ISSN:0840-7789
2576-7046
DOI:10.1109/CCECE.2001.933681