Physical and AI Based Diagnostics of Electrical Machines

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Supervisor:
Dr. Vajda István
Department of Electric Power Engineering

The topic of my thesis is focused on the various types of electrical machine faults. I examine what are the main reasons of certain faults, what kind of mechanism causes these failures. I present several physical models which are useful to describe these mechanisms.

I deal with the most common artificial intelligence methods. Applications is presented through some samples.

The current signature analysis is a very useful tool to detect different faults. I demonstrate the usage of this technique, especially on harmonic detection. I summarize the effects of these harmonics and I present some detection methods.

In the final part I make a diagnosis on a synchronous generator. I show some simulation results. With FEMM and Agros2D finite element programs I simulate the airgap flux density and after with Matlab I evaluate the harmonic content of this sign.

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