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http://hdl.handle.net/10603/16451
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DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Gaussian and cauchy inspired pso algorithms for power system optimization | en_US |
dc.date.accessioned | 2014-02-27T08:41:21Z | - |
dc.date.available | 2014-02-27T08:41:21Z | - |
dc.date.issued | 2014-02-27 | - |
dc.identifier.uri | http://hdl.handle.net/10603/16451 | - |
dc.description.abstract | The main objective of this research work is to develop an enhanced newlineParticle Swarm Optimization (PSO) algorithm for solving various power newlinesystem generation scheduling problems. The enhanced PSO algorithm is newlinedeveloped from the conventional PSO algorithm by the combined application newlineof Gaussian and Cauchy distribution and hence it is appropriately termed as newlineGaussian Cauchy inspired Particle Swarm Optimization (GCPSO). The newlinemodifications made into the conventional PSO algorithm ensure more reliable newlineand faster convergence in obtaining a global optimal solution. The integrity of newlinethe proposed algorithm lies in the significance of dealing the optimization newlineproblems without placing any restrictions on the structure or type of the newlinefunction to be optimized. The various power system problems that are solved newlineusing GCPSO algorithm include: Economic Dispatch (ED), Economic newlineEmission Load Dispatch (EELD), Multi Constrained Economic Dispatch newline(MCED) with non-smooth fuel cost function, DC Optimal Power Flow newline(DCOPF), AC Optimal Power Flow (ACOPF), Security Constrained Optimal newlinePower Flow (SCOPF), Transient Stability Constrained OPF (TSCOPF), newlineOptimal Power Flow with Flexible AC Transmission System (FACTS) newlinecontrollers and wind farm integrated ED and OPF problems. Initially the ED problem is solved with the PSO algorithm and the newlinemajor shortcomings of the PSO algorithm are analyzed. The analysis reveals newlinecertain major limitations such as relatively large computational time, tendency newlinetowards premature convergence and search inconsistency. Hence there is a newlinenecessity to enhance the PSO algorithm. The feasible modifications using newlinevarious probability distributions that can be introduced into the PSO newlinealgorithm are investigated. From this investigation it is found that the newlineapplication of Gaussian and Cauchy distributions into the velocity update newlineequation are appropriate for enhancing the PSO algorithm. | en_US |
dc.format.extent | xxv, 172p. | en_US |
dc.language | English | en_US |
dc.relation | p.162-169. | en_US |
dc.rights | university | en_US |
dc.title | Application of gaussian and cauchy inspired pso algorithms for power system optimization problems | en_US |
dc.title.alternative | en_US | |
dc.creator.researcher | Muthu selvan N B | en_US |
dc.subject.keyword | Cauchy | en_US |
dc.subject.keyword | Electrical engineering | en_US |
dc.subject.keyword | Gaussian | en_US |
dc.subject.keyword | Power system optimization | en_US |
dc.description.note | Appendix p.147-161, References p.162-169. | en_US |
dc.contributor.guide | Somasundaram P | en_US |
dc.publisher.place | Chennai | en_US |
dc.publisher.university | Anna University | en_US |
dc.publisher.institution | Faculty of Electrical and Electronics Engineering | en_US |
dc.date.registered | n.d. | en_US |
dc.date.completed | 01/06/2012 | en_US |
dc.date.awarded | 30/06/2012 | en_US |
dc.format.dimensions | 21 cm. | en_US |
dc.format.accompanyingmaterial | None | en_US |
dc.source.university | University | en_US |
dc.type.degree | Ph.D. | en_US |
Appears in Departments: | Faculty of Electrical and Electronics Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 69.8 kB | Adobe PDF | View/Open |
02_certificates.pdf | 279.38 kB | Adobe PDF | View/Open | |
03_abstract.pdf | 378.11 kB | Adobe PDF | View/Open | |
04_acknowledgement.pdf | 92.04 kB | Adobe PDF | View/Open | |
05_contents.pdf | 1.18 MB | Adobe PDF | View/Open | |
06_chapter 1.pdf | 2.02 MB | Adobe PDF | View/Open | |
07_chapter 2.pdf | 3.5 MB | Adobe PDF | View/Open | |
08_chapter 3.pdf | 2.74 MB | Adobe PDF | View/Open | |
09_chapter 4.pdf | 1.91 MB | Adobe PDF | View/Open | |
10_chapter 5.pdf | 2.84 MB | Adobe PDF | View/Open | |
11_chapter 6.pdf | 236.93 kB | Adobe PDF | View/Open | |
12_appendix.pdf | 1.39 MB | Adobe PDF | View/Open | |
13_references.pdf | 942.63 kB | Adobe PDF | View/Open | |
14_publications.pdf | 182.19 kB | Adobe PDF | View/Open | |
15_vitae.pdf | 47.2 kB | Adobe PDF | View/Open |
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