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

29 Scopus citations

Abstract

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
Volume1
StatePublished - Dec 1 1989
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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