Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Third International Conference on Intelligent Sensing and Information Processing
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Integration of an artificial neural network to predict electrical energy consumption
ISTASC'08 Proceedings of the 8th conference on Systems theory and scientific computation
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Artificial neural networks using pattern recognition methodology for security assessment of electric power systems is presented. Conventional numerical methods are either too complex or time consuming. An alternative method using neural networks to address the security assessment problem and its effectiveness against conventional methods is discussed. Neural networks using pattern recognition techniques is a promising methodology for different types of security assessment. Feature selection and extraction are used for selecting best features having highest discriminating capabilities. An important feature of the approach is that it can be generalized for steady state, transient and dynamic security assessment, which is a desirable feature for on-line security analysis. The proposed approach has been tested on the WSCC 9-bus 3-generator system. Steady state, transient and dynamic security assessment classification and contingency ranking results are provided to highlight the overall classification accuracy and suitability of the approach.