Alice Maimeri

Convergence region and estimation error of high frequency injection sensorless control strictly depend on the selected synchronous machine and on the actual operating point. An accurate knowledge of the motor parameters is required to compute the self-sensing capability range. The aim of this article is to show a fast solution to identify the convergence region based on incremental parameters approximation with less experimental measurements than the conventional approach. The proposed method to reconstruct the motor convergence region is fully offline. It requires few samples of the inverse incremental inductances, which are processed through some math and a simple fitting procedure. The method is validated through experimental measurements for two different types of synchronous motors for a sensorless drive application.

This paper deals with the sensorless detection of rotor position of synchronous reluctance motors in the low speed operating region. In particular, the position estimation is obtained by means of an high frequency voltage signal injection. The convergence and the estimation error of all such sensorless control algorithms of reluctance synchronous motors result troublesome. In fact, they strictly depend on the current working point where they operate. In order to evaluate the self-sensing capability range, an accurate modeling of motor parameters should be required. This paper proposes an innovative solution to identify the self-sensing convergence region through a differential parameters approximation. Advantages of this method include a lower commissioning effort and a reduced computational burden maintaining accurate reliability. The new approach was validated using experimental measurements.