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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.


ANN ADALINE model.
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sensors-15-15311-f004: ANN ADALINE model.

Mentions: In this section, The ADALINE structure is explained in [10]. The ANN-ADALINE structure is equivalent to one neuron which is composed of an input vector Xk, a weight matrix Wk, and an activation function f(v). The weights vector Wk = [W0k,W1k … Wnk]T corresponds to the whole neuron synaptic forces. The input vector Xk = [X0k, X1k … Xnk] corresponds to the whole neuron input stimulus. The activation function f(v) specifies the neuron behavior. Various activation functions can be used in the ANN theory. However, the ADALINE uses the linear activation function f(v) = v Consequently, the ADALINE output yk is given by: yk = XkWk. When the ADALINE is excited, it produces the output yk which depends on the input vector. The basic scheme of an ADALINE structure is shown in Figure 4. The weights vector Wk is continuously modified during the network learning process with the purpose of approaching as close as possible the desired output dk.


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)

ANN ADALINE model.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15311-f004: ANN ADALINE model.
Mentions: In this section, The ADALINE structure is explained in [10]. The ANN-ADALINE structure is equivalent to one neuron which is composed of an input vector Xk, a weight matrix Wk, and an activation function f(v). The weights vector Wk = [W0k,W1k … Wnk]T corresponds to the whole neuron synaptic forces. The input vector Xk = [X0k, X1k … Xnk] corresponds to the whole neuron input stimulus. The activation function f(v) specifies the neuron behavior. Various activation functions can be used in the ANN theory. However, the ADALINE uses the linear activation function f(v) = v Consequently, the ADALINE output yk is given by: yk = XkWk. When the ADALINE is excited, it produces the output yk which depends on the input vector. The basic scheme of an ADALINE structure is shown in Figure 4. The weights vector Wk is continuously modified during the network learning process with the purpose of approaching as close as possible the desired output dk.

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.