Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/19480
Author(s): J. A. Peças Lopes
M. H. Vasconcelos
Title: On-line dynamic security assessment based on kernel regression trees
Issue Date: 2000
Abstract: This paper presents a new approach to perform online dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique-kernel regression trees. This technique, besides producing fast security classification, can still quantify, in real-time, the security degree of the system, by emulating continuous security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like in the Crete island electric network (Greece). Comparative results regarding performances of decision trees and neural networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existing methods. © 2000 IEEE.
Subject: Engenharia electrotécnica, Engenharia electrotécnica, electrónica e informática
URI: http://hdl.handle.net/10216/19480
Source: Proceedings of the IEEE PES Winter Power Meeting' 2000
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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