Screening power system contingencies using a back-propagation trained multiperceptron

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

Screening power system contingencies using a back-propagation trained multiperceptron

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

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	486-489
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	1
State	Published - Dec 1 1989
Externally published	Yes

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Cite this

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title = "Screening power system contingencies using a back-propagation trained multiperceptron",

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.",

author = "R. Fischl and Moshe Kam and Chow, {J. C.} and S. Ricciardi",

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AU - Ricciardi, S.

PY - 1989/12/1

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N2 - 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.

AB - 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.

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Screening power system contingencies using a back-propagation trained multiperceptron

Abstract

All Science Journal Classification (ASJC) codes

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