Neural Speed-Torque Estimator for Induction Motors in the Presence of Measurement Noise

Sagar Verma, Nicolas Henwood, Marc Castella, Al Kassem Jebai, Jean-Christophe Pesquet

Abstract: In this article, a neural network (NN) approach is introduced to estimate the nonnoisy speed and torque from noisy measured currents and voltages in induction motors with variable speed drives. The proposed estimation method is comprised of a neural speed–torque estimator and a neural signal denoiser. A new training strategy is introduced that combines large amount of simulated data and a small amount of real-world data. The proposed denoiser does not require nonnoisy ground-truth data for training, and instead uses classification labels that are easily generated from real-world data. This approach improves upon existing noise removal techniques by learning to denoise as well as classify noisy signals into static and dynamic parts. The proposed NN-based denoiser generates clean estimates of currents and voltages that are then used as inputs to the NN estimator of speed and torque. Extensive experiments show that the proposed joint denoising-estimation strategy performs very well on real data benchmarks. The proposed denoising method is shown to outperform several widely used denoising methods and a proper ablation study of the proposed method is conducted.