Title :
Development of genetic algorithm embedded Kohonen neural network for dynamic security assessment
Author :
El-Sharkawi, M.A. ; Huang, S.J.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
A Kohonen self-organizing neural network embedded with a genetic algorithm is proposed in this paper. The genetic algorithm is embedded to initiate the Kohonen classifiers. By the proposed approach, the neural network learning performance and accuracy are greatly enhanced. In addition, the genetic algorithm can successfully avoid the neural network from being trapped in a local minimum. The proposed method is developed and tested on an electric utility system to access its dynamic security
Keywords :
genetic algorithms; learning (artificial intelligence); power system analysis computing; power system security; self-organising feature maps; Kohonen classifiers initiation; Kohonen neural network; dynamic security assessment; electric utility system; embedded genetic algorithm; neural network learning performance; Clustering algorithms; Genetic algorithms; Industrial training; Neural networks; Power system dynamics; Power system security; Power systems; Software algorithms; Software testing; 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.1996.501042
