Experimental Benchmarking of Existing Offline Parameter Estimation Methods for Induction Motor Vector Control

Abstract

Induction motors dominate industrial applications due to their unwavering reliability. However, optimal vector control, critical for maximizing dynamic performance, hinges on accurate parameter estimation. This control strategy necessitates precise knowledge of the motor’s parameters, obtainable through experimentation or calculation based on its design specifications. Numerous methods, ranging from traditional to computational, have been proposed by various researchers, often relying on specific assumptions that might compromise the performance of modern motor control techniques. This paper meticulously reviews the most frequently utilized methods and presents experimental results from a single motor. We rigorously compare these results against established benchmark methods, including IEEE Standard 112-2017, and subsequently identify the superior approach, boasting a maximum error of only 6.5% compared to 19.65% for competing methods. Our study investigates the parameter estimation of induction motor. The methodology primarily utilizes RMS values for measurement tasks. Moreover, the impact of harmonics, particularly when an induction motor is supplied by an inverter is briefly addressed. The pioneering contribution of this work lies in pinpointing a more accurate parameter estimation method for enhanced vector control performance. These findings pave the way for exceptional vector control, particularly at lower speeds, ultimately elevating both vector control and drive performance.