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Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive.

Gutierrez-Villalobos JM, Rodriguez-Resendiz J, Rivas-Araiza EA, Martínez-Hernández MA - Sensors (Basel) (2015)

Bottom Line: These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes.The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime.Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

View Article: PubMed Central - PubMed

Affiliation: Laboratorio de Mecatrónica, Universidad Autónoma de Querétaro, Cerro de las Campanas, Col. Las Campanas, S/N, Queretaro 76010, Mexico. marcelino.gutierrez@uaq.mx.

ABSTRACT
Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

No MeSH data available.


(a) FOC without ANN estimator response and (b) FOC with the ANN-block.
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sensors-15-15311-f007: (a) FOC without ANN estimator response and (b) FOC with the ANN-block.

Mentions: Sensorless FOC without estimator algorithm was first tested with a specific velocity profile, which consists of speed steps applied at 180, 540, 860, 1260 and 1440 R.P.M. at 1 N·m torque, generated with the DC motor. This created disturbances that directly affected IM speed. Those effects decreased or increased when load was applied or removed. Figure 7a shows the error between the estimated velocity and the real one at low revolutions, however, as speed increases, estimated velocity gets closer to the real value. Figure 7b shows the estimated speed and the desire one with load applied, which consists of speed steps applied at 180 and 540 R.P.M. at 1 N·m torque, using the ANN block, introduced to the standard FOC. The real velocity was measured with a QD200 encoder connected directly to the IM shaft.


Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive.

Gutierrez-Villalobos JM, Rodriguez-Resendiz J, Rivas-Araiza EA, Martínez-Hernández MA - Sensors (Basel) (2015)

(a) FOC without ANN estimator response and (b) FOC with the ANN-block.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541832&req=5

sensors-15-15311-f007: (a) FOC without ANN estimator response and (b) FOC with the ANN-block.
Mentions: Sensorless FOC without estimator algorithm was first tested with a specific velocity profile, which consists of speed steps applied at 180, 540, 860, 1260 and 1440 R.P.M. at 1 N·m torque, generated with the DC motor. This created disturbances that directly affected IM speed. Those effects decreased or increased when load was applied or removed. Figure 7a shows the error between the estimated velocity and the real one at low revolutions, however, as speed increases, estimated velocity gets closer to the real value. Figure 7b shows the estimated speed and the desire one with load applied, which consists of speed steps applied at 180 and 540 R.P.M. at 1 N·m torque, using the ANN block, introduced to the standard FOC. The real velocity was measured with a QD200 encoder connected directly to the IM shaft.

Bottom Line: These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes.The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime.Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

View Article: PubMed Central - PubMed

Affiliation: Laboratorio de Mecatrónica, Universidad Autónoma de Querétaro, Cerro de las Campanas, Col. Las Campanas, S/N, Queretaro 76010, Mexico. marcelino.gutierrez@uaq.mx.

ABSTRACT
Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

No MeSH data available.