Estimación de la Resistencia del Rotor Usando una Red Neuronal Artificial en el Control Vectorial Indirecto del Motor de Inducción (Using an Artificial Neural Network as a Rotor Resistance Estimator in the Indirect Vector Control of an Induction Motor)

Huerta Gonzalez (phuertaglez@hotmail.com), Rodriguez Rivas (jjrodriguezr@ipn.mx), Torres Rodríguez (bonceci@yahoo.com.mx)


Instituto Politécnico Nacional
This paper appears in: Revista IEEE América Latina

Publication Date: June 2008
Volume: 6,   Issue: 2 
ISSN: 1548-0992


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.

Index Terms:
Induction motor vector control, rotor resistance estimation, artificial neural network.   


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