期刊名称：International Journal on Electrical Engineering and Informatics
印刷版ISSN：2085-6830
出版年度：2014
卷号：6
期号：3
DOI：10.15676/ijeei.2014.6.3.1
出版社：School of Electrical Engineering and Informatics
摘要：In this paper, critical conditions in electric power systems are monitored byapplying various neural networks. In order to accomplish the stated goal, the authorstried several combinations of Feed Forward Neural Network and Layer RecurrentNeural Networks by imparting appropriate training schemes through supervisedlearning in order to formulate a comparative analysis on their performance. Once,training goes successful, the neural network learns how to deal with a set of newlypresented data through validation and testing mechanism so as to evolve the bestnetwork structure and learning criteria. The proposed methodology has been tested onthe standard IEEE 30-bus test system with the support of MATLAB based neuralnetwork toolbox. The results presented in this paper signify that the multi-layered feedforward neural network with Levenberg-Marquardt back propagation algorithm givesbest training performance of all possible cases considered in this paper, thus validatingthe proposed methodology.
关键词：L-index; LCI; neural network; feed forward; back propagation Introduction
