Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4559
Title: Intelligent systems techniques for electrical power systems fault location
Researcher: Chandra Sekhar, P
Guide(s): Sanker Ram, B V
Keywords: Power system
Power Quality Problems
High voltage spikes
Transients
Voltage sag magnitude
Electrical faults
Neuro computing
Electrical Engineering
Upload Date: 5-Sep-2012
University: Jawaharlal Nehru Technological University
Completed Date: March, 2012
Abstract: Modern power system network consists of several equipments like synchronous generators, transformers, transmission lines, bus- bars, loads etc; operating at different capacities and voltage levels. As a result, the complexity of the power system network has been increased. Due to this the power system network is frequently subjected to disturbances like short circuits, faults.In this work an approach to minimize neural network training error has been developed. Even the minor disturbances are detected and classified accurately using neural networks and wavelet transforms. The developed algorithm is implemented on a system subjected to different fault conditions.In this work an algorithm is developed to overcome the disturbances and accurately detect and classify the disturbances in the signal. The developed algorithm is independent of load voltage and customized for several sampling frequencies. The algorithm is tested on a distributed network having 10 nodes for several fault conditions and the result obtained is accurate, thereby providing an efficient architecture for the development of fully automated monitoring systems with classification ability in distributed power system. Among the components in a power system network, transmission line experiences most of the disturbances as it is exposed to the environment. In this work a fully connected multilayer feed forward neural network is trained with a supervised learning algorithm known as Back Propagation Algorithm (BPA).The neural fault detectors and locators are trained and tested for several fault conditions (fault types, fault locations, fault resistances and fault inception angles) and various power system data (source capacities, source voltages, source angles, time constants of the sources in a selected network model.Further, fuzzy neural network architecture for digital fault processing in distributed network is developed.
Pagination: xvii, 155p.
URI: http://hdl.handle.net/10603/4559
Appears in Departments:Department of Electrical and Electronics Engineering

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01_title.pdfAttached File76.25 kBAdobe PDFView/Open
02_declaration.pdf88.03 kBAdobe PDFView/Open
03_certificate.pdf124.58 kBAdobe PDFView/Open
04_acknowledgements.pdf110.16 kBAdobe PDFView/Open
05_abstract.pdf72.71 kBAdobe PDFView/Open
06_contents.pdf91.92 kBAdobe PDFView/Open
07_list of figures.pdf183.59 kBAdobe PDFView/Open
08_list of tables.pdf78.19 kBAdobe PDFView/Open
09_list of symbols and abbreviations.pdf131.61 kBAdobe PDFView/Open
10_chapter 1.pdf165.81 kBAdobe PDFView/Open
11_chapter 2.pdf312.29 kBAdobe PDFView/Open
12_chapter 3.pdf1.41 MBAdobe PDFView/Open
13_chapter 4.pdf2.43 MBAdobe PDFView/Open
14_chapter 5.pdf75.71 kBAdobe PDFView/Open
15_references.pdf123.8 kBAdobe PDFView/Open
16_appendices.pdf176.49 kBAdobe PDFView/Open


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