PROJECT TITLE :

Type-V Exponential Regression for Online Sensorless Position Estimation of Switched Reluctance Motor

ABSTRACT:

The idea of sensorless position sensing of switched reluctance motor (SRM) is enticing to researchers because of the increased reliability, robustness, and value reduction compared to standard drives. Sensorless drive is significantly helpful in electrical transportation applications where the setting is too hostile for physical position sensors, like within an electrical automotive or bus. This paper presents a replacement methodology to estimate the motor positions throughout startup or at flying restart. Unlike most of the strategies described within the literature, the algorithm, based mostly solely on the final magnetic characteristics of an SRM, can provide precise rotor positions while not specific motor magnetic information. The calculation is straightforward and will be implemented easily and efficiently with a microcontroller by users in business.


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