Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/333329
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dc.coverage.spatialImplementation of resilient directed neural network controlled virtual z source multilevel inverter fed brushless dc motor drive
dc.date.accessioned2021-07-26T07:00:53Z-
dc.date.available2021-07-26T07:00:53Z-
dc.identifier.urihttp://hdl.handle.net/10603/333329-
dc.description.abstractThe specific features such as operations at all speeds, low rotor inertia, better dynamic characteristics and rated starting current attracts industrial applications to go for Permanent Magnet Brushless DC motors rather than Induction motor. Similarly superior efficiency, long life, smooth torque delivery and high speed operation of these motors lead the brushed DC motors in the application field such as automotive, HVAC, electronic, computer, semiconductor and medical industries too. Permanent Magnet BLDC motor implies electronic commutation through the converter circuit and switches of those converters need to be controlled with an optimum controller. Effectiveness of speed response relies on both the Converter circuit and Controller. BLDC motors are generally provided with the conventional Proportional Integral Derivative (PID) controllers fed with the traditional Voltage Source and Current Source Inverters. Two level VSI and CSI suffer from common restrictions of harmonics distortion, high DC link voltage, high dv/dt, limited output voltage, vulnerability to EMI noise and so on. Hence these traditional converters can be replaced with the combination of Z Source and Multilevel Inverter circuits. Increase in the number of steps of voltage is the solution provided by the MLI. Reliability of the converter circuit is increased by realizing ZSI which also supports in buck and boost operation. Implementation of classical controllers with simple control structure leads to high overshoot in the response. Optimal selection of converters and controllers takes the Permanent Magnet BLDC drive system with reduced peak overshoot, steady state error, rise time, settling time and good dynamic behaviour. newline
dc.format.extentxvii,162p.
dc.languageEnglish
dc.relationp.152-161
dc.rightsuniversity
dc.titleImplementation of resilient directed neural network controlled virtual z source multilevel inverter fed brushless dc motor drive
dc.title.alternative
dc.creator.researcherSivaranjani, S
dc.subject.keywordNeural network
dc.subject.keywordDC motors
dc.subject.keywordConverter circuit
dc.description.note
dc.contributor.guideRajeswari, R
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Electrical Engineering

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01_title.pdfAttached File19.72 kBAdobe PDFView/Open
02_certificates.pdf461.12 kBAdobe PDFView/Open
03_vivaproceedings.pdf279.44 kBAdobe PDFView/Open
04_bonafidecertificate.pdf496.49 kBAdobe PDFView/Open
05_abstracts.pdf82.96 kBAdobe PDFView/Open
06_acknowledgements.pdf589.14 kBAdobe PDFView/Open
07_contents.pdf386.03 kBAdobe PDFView/Open
08_listoftables.pdf167.12 kBAdobe PDFView/Open
09_listoffigures.pdf413 kBAdobe PDFView/Open
10_listofabbreviations.pdf83.94 kBAdobe PDFView/Open
11_chapter1.pdf498.17 kBAdobe PDFView/Open
12_chapter2.pdf629.44 kBAdobe PDFView/Open
13_chapter3.pdf784.35 kBAdobe PDFView/Open
14_chapter4.pdf1.17 MBAdobe PDFView/Open
15_chapter5.pdf431.87 kBAdobe PDFView/Open
16_conclusion.pdf24.96 kBAdobe PDFView/Open
17_references.pdf185.69 kBAdobe PDFView/Open
18_listofpublications.pdf126.39 kBAdobe PDFView/Open
80_recommendation.pdf156.67 kBAdobe PDFView/Open


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