Estimación de la resistencia del rotor usando una red neuronal artificial en el control vectorial indirecto del motor de inducción

Translated title of the contribution: Using an artificial neural network as a rotor resistance estimator in the indirect vector control of an induction motor

Pedro Francisco Huerta González, Jaime J. Rodríguez Rivas, Ivone Cecilia Torres Rodríguez

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper presents a rotor resistance estimator based on an artificial neural network (ANN) used in the indirect vector control (IVC) of an induction motor (IM). Attention is focused on the dynamic performance of ANN rotor estimator, which gives superior performance over the fuzzy logic based rotor estimator reported in technical literature. The simulation was done using a 1.5 HP induction motor. The same ANN rotor estimator was proved with other IM having different rated power. The use of the same ANN was possible because the scaling and descaling (normalization) of the input and output of ANN was property done for each motor. The ANN training was done offline using the Levenberg-Marquardt algorithm. The neuronal network is a three-layer network; the first layer has fourteen neurons (or nodes), the hidden layer has five neurons and the output layer has only one neuron because the unique output signal is the rotor resistance value.

Translated title of the contributionUsing an artificial neural network as a rotor resistance estimator in the indirect vector control of an induction motor
Original languageSpanish
Pages (from-to)176-183
Number of pages8
JournalIEEE Latin America Transactions
Volume6
Issue number2
DOIs
StatePublished - 2008

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