پديد آورنده :
قويدل، مرتضي
عنوان :
تشخيص عيب توربين بادي با استفاده از روش هاي مبتني بر داده
مقطع تحصيلي :
كارشناسي ارشد
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر
صفحه شمار :
دوازده، 68ص.: مصور، جدول، نمودار
استاد راهنما :
جواد عسگري، ايمان ايزدي
توصيفگر ها :
SVM , شبكه عصبي
تاريخ نمايه سازي :
1394/06/02
تاريخ ورود اطلاعات :
1396/10/04
رشته تحصيلي :
برق و كامپيوتر
دانشكده :
مهندسي برق و كامپيوتر
چكيده فارسي :
به فارسي و انگليسي
چكيده انگليسي :
Fault detection of wind turbine using data mining Morteza Ghavidel M Ghavidel@ec iut ac ir Date of Submission 2015 03 01 Department of Electrical And Computer Engineering Isfahan University of Technology Isfahan 84156 83111 IranDegree M Sc Language FarsiSupervisors J Askari J Askari@cc iut ac ir I Izadi Iman Izadi@cc iut ac irAdvisor A Tabesh A Tabesh@cc iut ac irAbstract With the ever increasing population and the advancement of technology high energy consumptionis growing rapidly As a result use of alternative sources for fossil fuels is essential One of the mostcommon and abundant sources of energy is the wind Wind turbines as the most widely used systemfor generating electricity from the wind are made up of many components and treated as a complexsystem Similar to any other complex system wind turbines are susceptible to different types of faultsthat if not diagnosed and treated properly could lead to safety hazards and substantial losses Manymethods have been proposed for fault diagnosis in the past decades A category of methods that donot explicitly require a mathematical model of the system are data based techniques Support VectorMachine SVM and Neural Networks are two of the most popular of these methods In this thesis wind turbine fault diagnosis based on SVM is developed and compared to neural networks The datarequired for this thesis is obtained from a benchmark wind turbine model which is widely used amongresearchers in this field Five different faults in two distinct operation regions of the benchmark windturbine model are considered The output power of the wind turbine and the wind speed are collectedand used for fault diagnosis The collected data is broken into two parts which were used for trainingand validation of the proposed diagnosis framework Using data from the training set an SVM modelis trained to separate the faulty and normal states of the wind turbine operation The trained model isthen validated using the validation data All these steps were carried out for a neural network basedmodel as well and the results were compared with those obtained from the SVM model Finally adiscussion on the results and some suggestions are given for future work Keywords Wind turbine Fault detection Methods based on the data SVM Neural network
استاد راهنما :
جواد عسگري، ايمان ايزدي
