Neural network modeling of torque estimation and d-q transformation for induction machine

  • Authors:

  • Kaijam M. Woodley;Hui (Helen) Li;Simon Y. Foo

  • Affiliations:

  • Department of Electrical & Computer Engineering, FAMU-FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA;Department of Electrical & Computer Engineering, FAMU-FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA;Department of Electrical & Computer Engineering, FAMU-FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2005

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Abstract

This paper presents a neural network approach in modeling of torque estimation and Parks d-q transformation for an open-loop induction machine. The nonlinear approximation capability of neural networks makes it possible to map the Parks d-q transformation and torque estimation in an induction motor, which would otherwise require extensive complex calculations. The neural network simulation results will be compared to those of directly DSP calculated transformation and estimation. The results show improved performance with the neural network approach. We conclude that machine systems transformations and estimations can take advantage of the neural network technology for improved performance and cost reduction in the long run.