Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/10314
Title: Applications of evolutionary algorithms and neural network for congestion management in power systems
Researcher: Sujatha Balaraman
Guide(s): Kamaraj, N.
Keywords: Congestion Management, evolutionary algorithms, evolutionary programming, particle swarm optimization, real coded genetic algorithm, hybrid particle swarm optimization, differential equation, power system
Upload Date: 5-Aug-2013
University: Anna University
Completed Date: 2011
Abstract: In power Systems, under deregulated environment, transmission line congestion has become more intensified and recurrent, as compared to conventional regulated power System. One of the most practiced and an obvious technique of Congestion Management (CM) is rescheduling the power outputs of generators in the System. In this work, an efficient method for managing the congestion is presented for a day ahead electricity market using generation rescheduling as corrective measures. The objective of the study is to find the optimal generation values that minimize the total cost for congestion management based on the bids submitted by Generation Companies (GENCOs). The CM problem is solved using various evolutionary methods like Evolutionary Programming (EP), Real Coded Genetic Algorithm (RCGA), Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO) and Differential Evolution (DE). Their performances are tested on three test Systems, namely WSCC Nine Bus, IEEE30 Bus and IEEE118 Bus Systems. Through the test results, it is proved that the proposed DE yields best solutions than the other approaches and retains consistency and accuracy even for a large System. However, to minimize the computational time for large System, Cascade Neural Network (CNN) is proposed for congestion management in real time applications. For training the proposed CNN, two training methods Back Propagation (BP) and Radial Basis Function (RBF) are applied and the network is tested with the above-said test Systems. From the test results, it is shown that RBF learning method is found to be faster than BPN method, and also the classification accuracy is improved in RBF. Moreover, angular distance based clustering method is applied for selecting the input features of IEEE118 Bus System in order to reduce the dimension of neural network structure. The proposed methods can be applied for congestion management in off-line and on-line mode as well. newline
Pagination: xx, 165
URI: http://hdl.handle.net/10603/10314
Appears in Departments:Faculty of Electrical and Electronics Engineering

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01_title.pdfAttached File49.75 kBAdobe PDFView/Open
02_certificates.pdf733.38 kBAdobe PDFView/Open
03_abstract.pdf14.67 kBAdobe PDFView/Open
04_acknowledgement.pdf14.21 kBAdobe PDFView/Open
05_contents.pdf53.88 kBAdobe PDFView/Open
06_chapter 1.pdf41 kBAdobe PDFView/Open
07_chapter 2.pdf106.47 kBAdobe PDFView/Open
08_chapter 3.pdf213.37 kBAdobe PDFView/Open
09_chapter 4.pdf260.09 kBAdobe PDFView/Open
10_chapter 5.pdf22.9 kBAdobe PDFView/Open
11_appendix 1.pdf136.11 kBAdobe PDFView/Open
12_references.pdf39.49 kBAdobe PDFView/Open
13_publications.pdf17.25 kBAdobe PDFView/Open
14_vitae.pdf13.33 kBAdobe PDFView/Open


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