TDNN speed estimator applied to stator oriented IM sensorless drivers

2021 ◽  
Author(s):  
Tiago Henrique dos Santos ◽  
Ivan Nunes da Silva ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Bruno Augusto Angélico
Keyword(s):  
Speed Estimator

Speed Estimator to Improve Efficiency in a Wind Generation System

2019 ◽  
Author(s):  
Daniel Memije Garduno ◽  
Oscar Carranza Castillo ◽  
Jaime Jose Rodriguez Rivas ◽  
Ruben Ortega Gonzalez ◽  
Edgar Peralta Sanchez
Keyword(s):  
Wind Generation ◽  
Generation System ◽  
Improve Efficiency ◽  
Speed Estimator ◽  
Wind Generation System

scholarly journals A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

2021 ◽  
Vol 11 (6) ◽  
pp. 2809
Author(s):  
Dongmin Zhang ◽  
Qiang Song ◽  
Guanfeng Wang ◽  
Chonghao Liu
Keyword(s):  
Control Strategies ◽  
Low Cost ◽  
Estimation Error ◽  
Slip Rate ◽  
Vehicle Speed ◽  
Acceleration Sensor ◽  
Wheel Slip ◽  
Longitudinal Acceleration ◽  
Tire Rotation ◽  
Speed Estimator

This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel speed encoders and a longitudinal acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between the rotation tire radius and the manufacturer’s marked tire radius. The Local Vehicle Speed Estimator is defined to eliminate longitudinal vehicle speed estimation error. It improves the tire slip accuracy of four-wheel slip, even with a high slip rate. The final vehicle speed is estimated using two fuzzy control strategies that use vehicle speed estimates from speed encoders and a longitudinal acceleration sensor. Experimental and simulation results confirm the algorithm’s validity for estimating longitudinal vehicle speed for four-wheel slip in snowy road conditions.

Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives

2013 ◽  
Vol 62 (1) ◽  
pp. 25-41 ◽  
Author(s):  
K. Sedhuraman ◽  
S. Himavathi ◽  
A. Muthuramalingam
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Adaptive System ◽  
Learning Algorithm ◽  
Motor Drives ◽  
Speed Estimation ◽  
Induction Motor Drives ◽  
Model Reference ◽  
Neural Learning ◽  
Speed Estimator

Abstract This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

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