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Book Chapter
Machine Learning Models for Online Dynamic Security Assessment of Electric Power Systems
Book Series
Lecture Notes in Computer Science
Publisher
Springer Berlin / Heidelberg
ISSN
0302-9743 (Print) 1611-3349 (Online)
Volume
Volume 2527/2002
Book
Advances in Artificial Intelligence — IBERAMIA 2002
DOI
10.1007/3-540-36131-6
Copyright
2002
ISBN
978-3-540-00131-7
DOI
10.1007/3-540-36131-6_53
Pages
519-525
Subject Collection
Computer Science
SpringerLink Date
Tuesday, January 01, 2002
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Machine Learning Models for Online Dynamic Security Assessment of Electric Power Systems
Claudio M. Rocco
3
and José A. Moreno
3
(3)
Facultad de Ingeniería, Universidad Central, Apartado 47937, 1040A Caracas, Venezuela
Abstract
In this paper we compare two machine learning algorithms (Support Vector Machine and Multi Layer Perceptrons) to perform on-line dynamic security assessment of an electric power system. Dynamic simulation is properly emulated by training SVM and MLP models, with a small amount of information. The experiments show that although both models produce reasonable predictions, the performance indexes of the SVM models are better than those of the MLP models. However the MLP models are of considerably reduced complexity.
Claudio
M.
Rocco
Email:
rocco@neurona.ciens.ucv.ve
José
A.
Moreno
Email:
jose@neurona.ciens.ucv.ve
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