Power system loading margin estimation using a neuro-fuzzy approach

dc.creatorTorres, SR
dc.creatorPeralta, WH
dc.creatorCastro, CA
dc.date2007
dc.dateNOV
dc.date2014-11-14T23:29:18Z
dc.date2015-11-26T16:08:57Z
dc.date2014-11-14T23:29:18Z
dc.date2015-11-26T16:08:57Z
dc.date.accessioned2018-03-28T22:57:32Z
dc.date.available2018-03-28T22:57:32Z
dc.identifierIeee Transactions On Power Systems. Ieee-inst Electrical Electronics Engineers Inc, v. 22, n. 4, n. 1955, n. 1964, 2007.
dc.identifier0885-8950
dc.identifierWOS:000250559200060
dc.identifier10.1109/TPWRS.2007.907380
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/81149
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/81149
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/81149
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1266495
dc.descriptionFast 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.
dc.description22
dc.description4
dc.description1955
dc.description1964
dc.languageen
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.publisherPiscataway
dc.publisherEUA
dc.relationIeee Transactions On Power Systems
dc.relationIEEE Trans. Power Syst.
dc.rightsfechado
dc.rightshttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dc.sourceWeb of Science
dc.subjectadaptive neuro fuzzy inference systems (ANFIS)
dc.subjectfuzzy logic
dc.subjectloading margin
dc.subjectneuro-fuzzy
dc.subjectsubtractive clustering
dc.subjectvoltage security
dc.subjectvoltage stability
dc.subjectVoltage Collapse
dc.titlePower system loading margin estimation using a neuro-fuzzy approach
dc.typeArtículos de revistas


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