Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9650586 | Engineering Applications of Artificial Intelligence | 2005 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Kaijam M. Woodley, Hui (Helen) Li, Simon Y. Foo,