Screening power system contingencies using a back-propagation trained multiperceptron

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

Screening power system contingencies using a back-propagation trained multiperceptron

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

Research output: Contribution to journal › Conference 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 - 1989
Externally published	Yes
Event	IEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1 - Portland, OR, USA Duration: May 8 1989 → May 11 1989

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 M. Kam and Chow, {J. C.} and S. Ricciardi",

year = "1989",

language = "English (US)",

volume = "1",

pages = "486--489",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "IEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1 ; Conference date: 08-05-1989 Through 11-05-1989",

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T1 - Screening power system contingencies using a back-propagation trained multiperceptron

AU - Fischl, R.

AU - Kam, M.

AU - Chow, J. C.

AU - Ricciardi, S.

PY - 1989

Y1 - 1989

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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M3 - Conference article

AN - SCOPUS:0024900991

SN - 0271-4310

VL - 1

SP - 486

EP - 489

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

T2 - IEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1

Y2 - 8 May 1989 through 11 May 1989

ER -

Screening power system contingencies using a back-propagation trained multiperceptron

Abstract

All Science Journal Classification (ASJC) codes

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