11433

10509

كارشناسي ارشد

قدرت

اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر

1395

ده، 102ص.: مصور

ص. ع. به فارسي و انگليسي

مهدي معلم

محسن دوازده امامي

احمدرضا تابش، ابوالقاسم زيدآبادي نژاد

1395/05/25

كتابنامه

برق و كامپيوتر

مهندسي برق و كامپيوتر

ID10509

Fault detection in IPMSM stator winding using coupled electromagnetic thermal model Mohammad Bagher Aghashahi mb aghashahi @ec iut ac ir Data of Submission 06 06 2016 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran 84156 83111Degree Master of Science Language PersianSupervisor Prof Mehdi Moallem moallem@cc iut ac irAbstract Temperature profile is one of the most important factors which can be used to predictremaining lifetime and fault progress of electrical machines Machine lifetime depends oninsulation lifetime which basically depends on temperature Torque and power density ofmachine depends on current density too Losses and temperature are limitting factors ofcurrent density in electrical machines Thermal analysis is a tool for machine coolingdesign optimization and also can be used for fault diagnosis in electrical machines One of the most frequent faults that occur in an electric machine is stator winding fault This kind of fault is made by stator windings insulation breakdown due to over voltage temperature stress etc Induced voltage in shorted turns causes large circulating currentwhich makes high loss and heat in fault region So temperature profile on motor frame canbe used as a measure of fault diagnosis of stator winding faults In this thesis in order to obtain temperature profile on motor frame a coupledelectromagnetic thermal model is used This profile is computed for different states such ashealthy motor and motor with electrical faults inter turn short circuit fault phase to phaseand phase to ground fault and compared with healthy motor By comparison of simulationresults it is concluded that temperature profile for each fault is different from other types So with analysis of obtained profiles fault type can be identified Artificial neural networkis used for detection of faults by extracted features from temperature profiles Analysis ofthe results shows that standard deviation statistical parameter increases as fault severityrises So this parameter is used as an index for determination of fault severity Keywords thermal analysis fault diagnosis stator winding fault temperature profile artificial neural network

مهدي معلم

محسن دوازده امامي

احمدرضا تابش، ابوالقاسم زيدآبادي نژاد