Abstract
In this paper, a detailed study of stator current-based MRAS speed estimator is presented. Here, in this research work, a two-layer offline-trained feedforward neural network-based flux estimator is designed. The network proposed in this network is trained using Levenberg–Marquardt back-propagation learning algorithm using 5000 input–output samples obtained from the conventional simulation model. And the drive performance is tested for various criterions with the conventional model and neural network-based model including in low speed region and regenerative mode of operation. MATLAB/Simulink is used for experimentation purpose.
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Appendix
Appendix
Motor parameters
Parameter | Value |
|---|---|
Stator resistance (Rs) | 1.405 Ω |
Rotor resistance (Rr) | 1.395 Ω |
Stator inductance (Ls) | 0.005839 H |
Rotor inductance (Lr) | 0.005839 H |
Mutual inductance (Lm) | 0.1722 H |
Inertia (J) | 0.0131 kg m2 |
Pole pair (P) | 2 |
Friction factor (F) | 0.002985 Nm s |
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Goyal, T., Kumar, B. (2019). Analysis of Stator Current MRAS for Speed Estimation of Induction Motor Aided with ANN. In: Mishra, S., Sood, Y., Tomar, A. (eds) Applications of Computing, Automation and Wireless Systems in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-13-6772-4_37
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DOI: https://doi.org/10.1007/978-981-13-6772-4_37
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