Show simple item record

dc.contributor.authorBrar, Y S
dc.contributor.authorDhillon, Jaspreet S
dc.contributor.authorKothari, D P
dc.date.accessioned2005-12-30T04:25:48Z
dc.date.accessioned2019-02-10T12:56:10Z
dc.date.accessioned2019-02-11T06:21:56Z
dc.date.available2005-12-30T04:25:48Z
dc.date.available2019-02-10T12:56:10Z
dc.date.available2019-02-11T06:21:56Z
dc.date.issued2002
dc.identifier.citationElectric Power Systems Research, 63(2), 149-160en
dc.identifier.urihttp://localhost:8080/iit/handle/2074/1084
dc.description.abstractA multiobjective thermal power dispatch problem minimizes number of objectives viz cost and emission together while allocating the electricity demand among the committed generating units subject to physical and technological constraints. Such problems are solved to generate non-inferior solutions using weighting method or -constraint method. Afterwards the decision maker is provided with a set of simple but effective tools to choose the best alternative among non-inferior solutions. The generation of non-inferior solution requires an enormous amount of computation time when the number of objectives is more than two. In the paper, the multiobjective problem has been solved a using weighted technique. The Evolutionary optimization technique has been employed in which the ‘preferred’ weightage pattern has been searched to get the ‘best’ optimal solution in non-inferior domain. Decision making theories attempt to deal with the vagueness or fuzziness inherent in subjective or impressive determination of goals. So fuzzy set theory has been exploited to decide the ‘preferred’ optimal operating point. The non-inferior solution that attains maximum satisfaction level from the membership functions of the participating objectives has been adjudged the ‘best’ solution. The proposed method requires few search moves to get the optimal operating point in the non-inferior domain for any number of goals. The validity of the proposed method has been demonstrated on a 25 nodes IEEE system comprising five generators.en
dc.format.extent555812 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.subjectMultiobjective optimizationen
dc.subjectFuzzy seten
dc.subjectDecision makingen
dc.subjectMembership functionen
dc.subjectEvolutionary optimization techniqueen
dc.titleMultiobjective load dispatch by fuzzy logic based searching weightage patternen
dc.typeArticleen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record