Induction Machine Neural Estimator using Embedded System

Mouzinho, Lucilene Ferreira; FonsecaNeto, J.V.; Luciano, Benedito Antonio; Freire, R.C.S.; Catunda, S.Y.C.

doi:10.1109/imtc.2005.1604356

Cited by 1 publication

(3 citation statements)

References 6 publications

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“…The essential difference between this work and the published works in [18], [19] and [22] was the rotor speed indirect measurement via ANN, included the parametric uncertainties in the induction machine model. In the works mentioned previously was not considered of the robustness of the induction machine.…”

Section: Artificial Neural Network Architecturementioning

confidence: 99%

“…An analysis of performance of the use of ANN without robustness was accomplished [22]. The good performance of the estimator in state space was verified by comparison with the tachometer values in permanent regime.…”

Section: Artificial Neural Network Architecturementioning

confidence: 99%

“…4, 5 and 6. In[22] is used the same development of the models of a induction machine and obtains the rotor speed via ANN without robustness as show inFig. 7.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Robust ANN For Rotor Speed Indirect Measurement Of The Induction Machine

Mouzinho¹,

Neto²,

Luciano³

et al. 2016

Anais Do 8. Congresso Brasileiro De Redes Neurais

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This paper presents a method for the modeling of the rotor speed indirect measurement in an induction machine. It uses an Artificial Neural Network (ANN) algorithm. The algorithm is developed considering the robustness of the induction machine. This method is investigated and realized via environmental M AT LAB and C language. The induction machine model is developed and processed in M AT LAB, analyzing the behavior of stator voltage and current, rotor flux and rotor speed. These values (stator voltage and current) are storages in a file and, after used with step input signals for the ANN algorithm developed in C language. The rotor speed is one of the three weights of an ANN.

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Section: Artificial Neural Network Architecturementioning

confidence: 99%

Section: Artificial Neural Network Architecturementioning

confidence: 99%