The application of the finite element method in diagnostics of electrical drives

• Autorzy: Fiser R. , Ferkolj S.
• Języki publikacji: EN

Abstrakty

EN

The paper presents the application of the finite element method for predicting the performance of induction motor having asymmetry of stator and/or rotor winding due to the different combination and propagation of faults gained during heavy-duty operation. Quantities like magnetic vector potential, flux density, force components, rotor and stator currents, mutual and leakage inductance are determined very precisely. The detailed insight in magnetic field distribution of a squirrel cage induction motor, forms the basis for further evaluation of its operational behaviour. Increasing anomaly in magnetic field distribution due to the increasing stage of faults propagation results in a degradation of steady-state and dynamic performance of the induction motor and can be evaluated with computer simulation eliminating expensive and time-consuming laboratory tests.

Słowa kluczowe

EN

finite element analysis; induction motor drive; machine theory; magnetic field; magnetic flux; rotor; squirrel-cage motor; stator

PL

analiza elementu skończonego; napęd silnika indukcyjnego; teoria perturbacji; pole magnetyczne; strumień magnetyczny; wirnik; silnik klatkowy; stojan

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Journal of Technical Physics
• Rocznik: 2002
• Tom: Vol. 43, no 4
• Strony: 399--403
• Opis fizyczny: Bibliogr. 4 poz., wykr.

Twórcy

autor

Fiser R.

• Faculty of Electrical Engineering, University of Ljubljana, Trżaśka 25, SI-1000 Ljubljana, Slovenia

autor

Ferkolj S.

• Faculty of Electrical Engineering, University of Ljubljana, Trżaśka 25, SI-1000 Ljubljana, Slovenia

Bibliografia

• 1. P. Vas, Parameter estimation, condition monitoring, and diagnosis of electrical machines, Clarendon Press, Oxford 1993.
• 2. A.D. Pouliezos, G.S. Stavrakakis, Real time fault monitoring of industrial processes, Kluwer Academic Publishers, Dordrecht 1994.
• 3. P. Vas, Artificial-intelligence-based electrical machines and drives: Application of Juzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques, Oxford Press, New York 1999.
• 4. R. Fišer, Modelling and diagnosis of faults in induction motor drives, Ph.D. dissertation, Faculty of Electrical Engineering, Univ. Ljubljana 1998.