An improved Hopfield model for power system contingency classification

J. C. Chow, R. Fischl, M. Kam, H. H. Yan, S. Ricciardi

Research output: Contribution to journalConference articlepeer-review

19 Scopus citations

Abstract

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 constrast to a learning method) for find 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.

Original languageEnglish (US)
Pages (from-to)2925-2928
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume4
StatePublished - Dec 1 1990
Externally publishedYes
Event1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA
Duration: May 1 1990May 3 1990

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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