Sensorless Speed Estimation Basing on MRAS Model for a PMSM Machine Application

Wind energy systems utilizing synchronous machines can encounter challenges with speed detection at high rotational speeds due to increasing motor temperatures affecting parameters like stator resistance. This paper addresses these challenges by proposing a novel high-speed estimator algorithm based on the Model Reference Adaptive System (MRAS) approach. The primary contribution of this research is the development of an MRAS-based speed estimator that leverages a reactive power model to maintain robustness against variations in stator resistance, even at elevated speeds. To optimize the estimator’s performance, we employed a particle optimization algorithm for tuning, which overcomes issues related to regulator parameter identification. We implemented the proposed algorithm in Matlab and validated it on a real machine prototype capable of high-speed operation. After a comparison wth 5 different methods, the results indicate that the estimator performs effectively up to 42,000 RPM (600 Hz), demonstrating a maximum speed estimation error of 50 Hz. Stability analyses across various speed regions and practical lab tests confirm the robustness and accuracy of the proposed control scheme. The findings highlight the estimator’s improved performance in high-speed scenarios, showcasing its potential for enhancing speed detection in wind energy systems.