Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/427407
Title: Assessment on power quality issues in microgrid using deep learning techniques based DSTATCom control
Researcher: Prabaakaran, K
Guide(s): Kumar, C
Keywords: Microgrid
Learning techniques
DSTATCom
University: Anna University
Completed Date: 2021
Abstract: Renewable Energy Sources are those energy sources which are not newlinedestroyed when their energy is harnessed. Human use of renewable energy newlinerequires technologies that harness natural phenomena, such as sunlight, wind, newlinewaves, water flow, and biological processes such as anaerobic digestion. newlineApplications of renewable energy sources, such as solar cell array newlineand wind turbines have increased significantly during the past decade. newlineTo obtain clean energy, the hybrid solar-wind power generation is used. newlineThe electric power system consists of three major functional blocks, such as newlinegeneration, transmission and distribution. As per reliability consideration in newlinepower system, generation unit must generate adequate amount of power, newlinetransmission unit should supply maximum power over long distances without newlineoverloading and distribution system must deliver electric power to each newlineconsumer s premises. newlineDistribution system is located at the end of electric power system newlineand is directly related to the consumer, and hence the power quality depends newlineupon the state of distribution system. The reason for this is that failure in the newlineelectric distribution network accounts for about 91% of the average newlineconsumer s interruptions. Earlier, power system reliability focused on newlinegeneration and transmission system due to capital investment. But today, newlinedistribution system is receiving more attention as reliability is concerned. newlineConsumers prefer quality of power from suppliers, which can be measured by newlineusing parameters, such as voltage sag, harmonics and power factor. newline
Pagination: xxi,162p.
URI: http://hdl.handle.net/10603/427407
Appears in Departments:Faculty of Electrical Engineering

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02_prelim pages.pdf1 MBAdobe PDFView/Open
03_content.pdf141.97 kBAdobe PDFView/Open
04_abstract.pdf130.23 kBAdobe PDFView/Open
05_chapter 1.pdf452.11 kBAdobe PDFView/Open
06_chapter 2.pdf362.45 kBAdobe PDFView/Open
07_chapter 3.pdf567.51 kBAdobe PDFView/Open
08_chapter 4.pdf675.55 kBAdobe PDFView/Open
09_chapter 5.pdf1.85 MBAdobe PDFView/Open
10_annexures.pdf132.4 kBAdobe PDFView/Open
80_recommendation.pdf61.44 kBAdobe PDFView/Open
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