Neural Speed Estimation Applied to Stator Flux-Oriented Control Drives

The induction motor speed is an important quantity in an industrial process and can be used indirectly in flow, pressure, and drive control. However, the direct measurement of speed compromises the driver system and control, increasing the implementation cost. Speed estimators are usually based on a...

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Bibliographic Details
Published in:Electric power components and systems 2019-06, Vol.47 (9-10), p.798-809
Main Authors: Dos Santos, Tiago Henrique, Da Silva, Ivan Nunes, Goedtel, Alessandro, Castoldi, Marcelo Favoretto
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer simulation
Direct current
Directional control
Estimators
Evaluation
Induction motors
Neural networks
neural speed estimator
Parameter estimation
Performance evaluation
Robustness (mathematics)
sensorless control
Stators
Torque
variable speed drives
vector control drivers
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Summary:The induction motor speed is an important quantity in an industrial process and can be used indirectly in flow, pressure, and drive control. However, the direct measurement of speed compromises the driver system and control, increasing the implementation cost. Speed estimators are usually based on a mathematical model of the induction motor, but it is typically necessary to obtain the parameters of the motors. Thus, this work proposes an artificial neural network approach to estimate the mechanical speed of induction motors in a stator flux-oriented vector control by direct current control and direct torque control. In this proposed strategy, no machine parameters adaptation is needed. The neural speed estimators, without weight change, are tested in two different motors to evaluate its robustness. First, by simulation, the neural networks are trained with constant machine parameters and then the estimator performance is evaluated under stator and rotor resistance variation. After that, the same neural estimators are experimentally tested, with another machine, under the variation of motor load torque and speed reference operating point.
ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2019.1627613