Title :
Input dimension reduction in neural network training-case study in transient stability assessment of large systems
Author :
Muknahallipatna, Suresh ; Chowdhury, Badrul H.
Author_Institution :
Dept. of Electr. Eng., Wyoming Univ., Laramie, WY, USA
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
Keywords :
learning (artificial intelligence); neural nets; power system analysis computing; power system stability; power system transients; convergence problems; discriminant analysis; input dimension reduction; neural network training; power systems; training algorithm; transient stability assessment; Artificial neural networks; Backpropagation; Computer aided software engineering; Convergence; Intelligent networks; Neural networks; Power generation; Power system stability; Power system transients; System testing;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3115-X
DOI :
10.1109/ISAP.1996.501043
