Screening power system contingencies using a back-propagation trained multiperceptron

R. Fischl, Moshe Kam, J. C. Chow, S. Ricciardi

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


The utility of trained neural networks in calculating the network state and classifying its security status under different load and contingency conditions is demonstrated. In particular, a two-layer multiperceptron is used to screen contingent branch overloads. The performance of this approach is evaluated using a six-bus example. The results indicate that the proposed tasks can be performed reliably by back-propagation-trained multiperceptrons.

Original languageEnglish (US)
Pages (from-to)486-489
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
StatePublished - Dec 1 1989
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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