Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24146
Title: An evolutionary programming based efficient particle swarm optimization for economic dispatch problem with valve point loading
Researcher: Muthu vijaya pandian S
Guide(s): Thanushkodi K
Keywords: economic dispatch
electrical engineering
evolutionary programming
Upload Date: 27-Aug-2014
University: Anna University
Completed Date: 01/05/2011
Abstract: Economic dispatch ED is one of the most important newlineoptimization problems in a power system The objective of ED is to share newlinethe demand for power among the online generators keeping the minimum newlinecost of generation as a constraint It determines the optimal settings of newlinegenerator units with predicted load demand on a particular time interval newlineThe aim of the present thesis is to operate an electric power system most newlineeconomically within its security limits It is known that the security limit of newlineany generator lies between its minimum and maximum power generation newlinecapacity The present thesis mainly focuses on minimizing the total fuel newlinecost of all the generators in the power system newlineThe following two hybrid algorithms are proposed to solve the newlinenon convex economic dispatch problem newline newline
Pagination: xvii, 87p.
URI: http://hdl.handle.net/10603/24146
Appears in Departments:Faculty of Electrical and Electronics Engineering

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01_title.pdfAttached File176.31 kBAdobe PDFView/Open
02_certificates.pdf760.68 kBAdobe PDFView/Open
03_abstract.pdf60.63 kBAdobe PDFView/Open
04_acknowledgement.pdf58.97 kBAdobe PDFView/Open
05_contents.pdf117.78 kBAdobe PDFView/Open
06_chapter 1.pdf83.86 kBAdobe PDFView/Open
07_chapter 2.pdf185.51 kBAdobe PDFView/Open
08_chapter 3.pdf384.32 kBAdobe PDFView/Open
09_chapter 4.pdf249.01 kBAdobe PDFView/Open
10_chapter 5.pdf69.94 kBAdobe PDFView/Open
11_appendix.pdf72.83 kBAdobe PDFView/Open
12_references.pdf75.75 kBAdobe PDFView/Open
13_publications.pdf64.5 kBAdobe PDFView/Open
14_vitae.pdf51.32 kBAdobe PDFView/Open


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