Neural Speed–Torque Estimator for Induction Motors in the Presence of Measurement Noise
Author:
Affiliation:
1. CentraleSupélec, Inria, Centre de Vision Numérique, Université Paris-Saclay, Gif-sur-Yvette, France
2. Schneider Toshiba Inverter Europe, Pacy-sur-Eure, France
3. SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France
Funder
BRIDGEABLE ANR Research
Teaching Chair in Artificial Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/41/9866919/09724134.pdf?arnumber=9724134
Reference27 articles.
1. Sensorless Control of Induction Motor Drives at Very Low and Zero Speeds Using Neural Network Flux Observers
2. Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation
3. Modeling Electrical Motor Dynamics Using Encoder-Decoder with Recurrent Skip Connection
4. Neural Networks based Speed-Torque Estimators for Induction Motors and Performance Metrics
5. Noise reduction by vector median filtering
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1. Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors;Sensors;2024-04-19
2. Torque estimation in three-phase induction motors under broken bars and voltage variations using airgap flux measurement and a machine learning model;Measurement Science and Technology;2023-11-28
3. Convolutional-neural-network-based multi-signals fault diagnosis of induction motor using single and multi-channels datasets;Alexandria Engineering Journal;2023-07
4. Data-Driven Based Hybrid Predictive Model for the PMSM Drive System;2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE);2023-06-16
5. An Approach of Position and Torque Estimation for Induction Motor based Sensor-less Drive;International Journal of Circuits, Systems and Signal Processing;2023-03-06
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