Screening power system contingencies using a back-propagation trained multiperceptron

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

Research output: Contribution to journalConference articlepeer-review

31 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 - 1989
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1 - Portland, OR, USA
Duration: May 8 1989May 11 1989

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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