An improved Hopfield model for power system contingency classification

A method for designing neural networks (NNs) for classifying contingencies in terms of the number and type of limit violations is presented. Specifically, an optimization method (in contrast to a learning method) for finding the weights and thresholds of an associated Little-Hopfield NN is developed. This optimization method, which uses the linear programming technique, maximizes the probability of classifying the contingency correctly. The contingency classification problem is formulated into a pattern recognition problem. A NN to detect a prescribed set of patterns is then designed.<<ETX>>

[1]  J.-C. Chow,et al.  Screening power system contingencies using a back-propagation trained multiperceptron , 1989, IEEE International Symposium on Circuits and Systems,.

[2]  R.J. Marks,et al.  Artificial neural networks for power system static security assessment , 1989, IEEE International Symposium on Circuits and Systems,.

[3]  Edward H. P. Chan,et al.  APPLICATION OF NEURAL-NETWORK COMPUTING IN INTELLIGENT ALARM PROCESSING , 1989 .

[4]  R.J. Marks,et al.  Preliminary results on using artificial neural networks for security assessment (of power systems) , 1989, Conference Papers Power Industry Computer Application Conference.

[5]  O. Alsac,et al.  Security analysis and optimization , 1987, Proceedings of the IEEE.

[6]  Robert Fischl,et al.  Design of the fully connected binary neural network via linear programming , 1990, IEEE International Symposium on Circuits and Systems.

[7]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[8]  E.H.P. Chan,et al.  Application of neural-network computing in intelligent alarm processing (power systems) , 1989, Conference Papers Power Industry Computer Application Conference.

[9]  Mo-Yuen Chow,et al.  Neural network synchronous machine modeling , 1989, IEEE International Symposium on Circuits and Systems,.

[10]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.