Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4566
Title: Design and development of cluster algorithms for power system problems
Researcher: Amarnath, R V
Guide(s): Ramana, N V
Keywords: Electrical Engineering
Optimal Power Flow Problem
High Density Cluster
Genetic Algorithm
Upload Date: 5-Sep-2012
University: Jawaharlal Nehru Technological University
Completed Date: March, 2012
Abstract: This thesis presents design and development of genetic based cluster algorithm application to mitigate the complex power system problem namely, Optimal Power Flow (OPF) problem. Two individual objective functions are chosen: 1) minimization of Fuel Cost and 2) minimization of Power Loss. The proposed algorithm is the application of a multi-objective genetic algorithm (MOGA), using the combination of High Density cluster and continuous genetic algorithm. The OPF is modeled as a nonlinear, non-convex and large scale constrained problem with continuous variables. The algorithm uses a local search method for the search of Global optimum solution. Binary coded Genetic algorithm is replaced with continuous genetic algorithm that uses real values of generation instead of binary coded data. An attempt is made to reduce the length. Inspired by the results of Genetic Algorithm (GA) method and to overcome the general difficulties in GA approach, a novel method is proposed in this work.. The method uses high density cluster DBSCAN and Continuous GA algorithms. The new technique for the solution of OPF based on Genetic search from a High Density Cluster named in short form as ?GSHDC? is proposed in this thesis The objective of GSHDC is to retain advantages of Mathematical Programming techniques and to encounter the difficulties of evolutionary methods like GA and PSO Methods. The GSHDC has mainly four stages.Stage-1: In the first stage a suboptimal solution for OPF problem is obtained by any of the following local search methods 1) Modified Penalty Factor Method 2) Primal-Dual Interior Point method and 3) Particle Swarm Optimization Method that considers Lagrange multipliers, equality constraints, transmission loss B-Coefficients and penalty factors. Owing to the limitations of the methods, this solution is taken only as suboptimal or local optimal. Because of this reason, this OPF solution cannot be taken as a global one.
Pagination: v, 255p.
URI: http://hdl.handle.net/10603/4566
Appears in Departments:Department of Electrical and Electronics Engineering

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01_title.pdfAttached File45.31 kBAdobe PDFView/Open
02_certificate.pdf44.89 kBAdobe PDFView/Open
03_declaration.pdf35.22 kBAdobe PDFView/Open
04_abstract.pdf166.47 kBAdobe PDFView/Open
05_acknowledgements.pdf8.68 kBAdobe PDFView/Open
06_dedication.pdf15.93 kBAdobe PDFView/Open
07_table of contents.pdf54.2 kBAdobe PDFView/Open
08_list of figures.pdf29.94 kBAdobe PDFView/Open
09_list of tables.pdf62.71 kBAdobe PDFView/Open
10_notations.pdf110.95 kBAdobe PDFView/Open
11_chapter 1.pdf222.43 kBAdobe PDFView/Open
12_chapter 2.pdf260.91 kBAdobe PDFView/Open
13_chapter 3.pdf932.29 kBAdobe PDFView/Open
14_chapter 4.pdf400.79 kBAdobe PDFView/Open
15_chapter 5.pdf893.85 kBAdobe PDFView/Open
16_chapter 6.pdf204.99 kBAdobe PDFView/Open
17_references.pdf180.7 kBAdobe PDFView/Open
18_appendix.pdf588.63 kBAdobe PDFView/Open
19_researcher detail.pdf5.41 kBAdobe PDFView/Open


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