Abstract

The problem in modeling large systems by artificial neural networks (ANN) is that the size of the input vector can become excessively large. This condition can potentially increase the likelihood of convergence problems for the training algorithm adopted. Besides, the memory requirement and the processing time also increase. This paper addresses the issue of ANN input dimension reduction. Two different methods are discussed and compared for efficiency and accuracy when applied to transient stability assessment.

Meeting Name

International Conference on Intelligent Systems Applications to Power Systems, 1996

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Convergence Problems; Discriminant Analysis; Input Dimension Reduction; Learning (Artificial Intelligence); Neural Nets; Neural Network Training; Power System Analysis Computing; Power System Stability; Power System Transients; Power Systems; Training Algorithm; Transient Stability Assessment

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1996 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Share

 
COinS