6/8 dıştan rotorlu anahtarlamalı relüktans motorun modellenmesi ve denetimi

In this study, external rotor switched reluctance motor with six stator and eight rotor poles modelled in Matlab & Simulink and the controller was designed. The phase flux linkage was estimated with artificial neural networks in which solely the phase currents were used as the input. For this purpose, one type of artificial intelligence network, which is called "Nonlinear Autoregrassive Network with External Input - Narx Neural Network" was selected as the network architecture. The real-time application of the developed phase flux estimation algorithm was performed. The results were examined comparatively with the results obtained from other important phase flux estimation methods found in the literature such as finite element analysis method, torque balance method and voltage balance method in which the inputs of the phase flux estimator was phase currents and the rotor position. In addition, a patent application was made for the economic and easily applicable hall effect type rotor position detection system, developed throughout the Phd thesis study. The motor was successfully operated with PI controller using this rotor position detection system. The results obtained in this Phd thesis study showed that the phase flux linkage estimation of the switched reluctance motor can be achieved with the NARX Neural Network in which only the phase current is taken as the input, regardless of the rotor position information. In addition, the developed hall-effect sensor system contributed to the moment ripple of the external rotor - switched reluctance motor which was measured as 13%.