Self-Commissioning Parameter Estimation Algorithm for Loaded Induction Motors
Juan Carlos Travieso‐Torres, Sze Sing Lee, Adolfo Véliz-Tejo, Felipe Leiva-Silva, Abdiel Ricaldi-Morales
Abstract
Induction motor (IM) drive systems are pivotal in modern industrial and commercial applications, driving essential processes and systems across various sectors efficiently and reliably. The accurate estimation of IM and load parameters is challenging but vital to guarantee the optimal performance of the entire drive system. The IEEE Standard 112A and existing online methods take hours to estimate only IM parameters after shutting down, disconnecting, estimating, reconnecting, and realigning the motor and its load using specialized tools. Offline techniques assume a known IM manufacturer datasheet, which is often unavailable. Therefore, this article proposes a novel online self-commissioning algorithm that estimates the parameters of an IM and its load in a remarkably swift time frame of just 180 s without disconnecting the IM from the load or using the IM manufacturer datasheet. The proposed method only requires the IM nameplate information and employs a discrete normalized model reference adaptive system. In addition, its computational burden is minimal, making it suitable for practical real-time implementation using the commercial and cost-effective microcontroller for power electronics, i.e., c2000 TMS320F28388D control platform. Comprehensive experimental results for fan-type loads driven by 1.1- and 7.5-kW IMs are discussed to validate the proposal.