Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/81149
Type: Artigo de periódico
Title: Power system loading margin estimation using a neuro-fuzzy approach
Author: Torres, SR
Peralta, WH
Castro, CA
Abstract: Fast methods for estimating voltage stability security limits are crucial in modern energy management systems. In this paper, a method to build a fuzzy inference system (FIS) is developed in order to estimate the loading margin. The main goal is to overcome the disadvantages of conventional methods and to apply this methodology in a real time operation environment. First, some voltage stability indices and variables are presented as candidate inputs to the FIS. Subtractive clustering is used to construct the initial FIS models, and adaptive neuro fuzzy inference systems allow tuning them so that it is possible to obtain better loading margin estimates. Extensive simulations were carried out in order to build data sets that take into account a quasi-random load direction, as well as information regarding base case and contingency situations, including branch, generator, and shunt single outages. Results are provided for the IEEE 30, 118, and 300 bus test systems.
Subject: adaptive neuro fuzzy inference systems (ANFIS)
fuzzy logic
loading margin
neuro-fuzzy
subtractive clustering
voltage security
voltage stability
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Rights: fechado
Identifier DOI: 10.1109/TPWRS.2007.907380
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

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