Approximate Loading Margin Methods Using Artificial Neural Networks in Power Systems

Author

Sode-Yome, A. ; Lee, K.Y.

Author_Institution

Siam Univ., Bangkok, Thailand

fYear

2009

fDate

8-12 Nov. 2009

Firstpage

1

Lastpage

6

Abstract

This paper proposes approximate loading margin methods using artificial neural networks (NN) for static voltage stability in power systems. Two methodologies, namely actual LM with NN (ALM-NN) and maximum loading margin with NN (MLM-NN), are proposed for finding NN training data sets. Artificial neural network is used to approximate the loading margin at particular generation direction. The proposed methods are validated and compared with the maximum loading margin method in the modified IEEE 14-bus test system. The methods will help system operators to approximate voltage stability margin or loading margin of the system in a short period of time.

Keywords

neural nets; power engineering computing; power system stability; IEEE 14-bus test system; approximate loading margin; artificial neural networks; generation direction; power systems; static voltage stability margin; Artificial neural networks; Control systems; Neural networks; Power generation; Power system planning; Power system simulation; Power system stability; Power systems; Reactive power; Voltage; Loading margin; generation direction; maximum loading margin method; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on

Conference_Location

Curitiba

Print_ISBN

978-1-4244-5097-8

Type

conf

DOI

10.1109/ISAP.2009.5352868

Filename

5352868