Performance Analysis of Artificial Intelligent Based SVM Controller for an Inverter Fed Induction Motor Drive

dc.contributor.guideG. DURGA SUKUMAR
dc.coverage.spatial
dc.creator.researcherGADDE SRINIVAS
dc.date.accessioned2025-11-20T12:11:21Z
dc.date.available2025-11-20T12:11:21Z
dc.date.awarded2025
dc.date.completed2025
dc.date.registered2016
dc.description.abstractInduction motors are widely used across industries due to their affordability, newlinedurability, ease of operation, and nearly constant speed. Three-phase induction motors newlineaccount of nearly 90% of the electrical energy consumed in industrial applications newline.However, due to significant limitations in power output, research on enchanting the newlineefficiency of three phase induction motors is essential to reduce energy consumption. newlineWhen operate conventionally, several factors can adversely affect motor performance, newlineincluding torque ripples, generalized motor design, control methods that fail to account newlinefor variations in operating parameters, and design inaccuracies. newlineThe current study aims to improve the efficiency of induction motors for newlinecustomized applications such as production, steel mills, textile industries, etc. by newlineutilizing soft computing approaches. Therefore, the following are the primary newlinecontributions of the current study. When the operating point remains constant, the newlinetraditional speed and current controllers in indirect vector control function newlinesatisfactorily. However, the reference voltages obtained in a closed-loop system feeding newlinethe inverter contain more harmonics, and the operating point is always dynamic. The newlinepulses that will be produced as a result are unequal. Which intern generates the output newlinevoltages of the inverter, which have higher harmonics. This work investigates into the newlinedetails of the Type-2 Neuro-Fuzzy (T2NF) Space Vector Modulation (SVM) technique, newlinewhich enhances the output voltage of an inverter. Through a detailed comparison, the newlineT2NF approach is evaluated against both the Type-1 Neuro-Fuzzy (T1NF) system and newlinetraditional SVM methods. Utilizing MATLAB simulations coupled with rigorous newlineexperimental validation, this study differences the performance of these modulation newlinetechniques across several critical parameters speed, torque, and current of an induction newlinemotor. In a proposal to explore the efficacy of T2NF SVM, the research compares it newlinewith the T1NF and conventional SVM methodologies.
dc.description.note
dc.format.accompanyingmaterialCD
dc.format.dimensions
dc.format.extent148
dc.identifier.researcherid0009-0002-8519-5574
dc.identifier.urihttp://hdl.handle.net/10603/675283
dc.languageEnglish
dc.publisher.institutionDepartment of Electrical and Electronics engineering
dc.publisher.placeGuntur
dc.publisher.universityVignans Foundation for Science Technology and Research
dc.relation
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering
dc.subject.keywordEngineering Electrical and Electronic
dc.titlePerformance Analysis of Artificial Intelligent Based SVM Controller for an Inverter Fed Induction Motor Drive
dc.title.alternative
dc.type.degreePh.D.

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