International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol 13, No 2: June 2022

Non-parametric induction motor rotor flux estimator based on feed-forward neural network

Siti Nursyuhada Mahsahirun (Universiti Malaysia Pahang)
Nik Rumzi Nik Idris (Universiti Teknologi Malaysia)
Zulkifli Md. Yusof (Universiti Malaysia Pahang)
Tole Sutikno (Universitas Ahmad Dahlan)



Article Info

Publish Date
01 Jun 2022

Abstract

The conventional induction motor rotor flux observer based on current model and voltage model are sensitive to parameter uncertainties. In this paper, a non-parametric induction motor rotor flux estimator based on feed-forward neural network is proposed. This estimator is operating without motor parameters and therefore it is independent from parameter uncertainties. The model is trained using Levenberg-Marquardt algorithm offline. All the data collection, training and testing process are fully performed in MATLAB/Simulink environment. A forced iteration of 1,000-epochs is imposed in the training process. There are overall 603,968 datasets are used in this modeling process. This four-input two-output neural network model is capable of providing rotor flux estimation for field-oriented control systems with 3.41e-9 mse and elapsed 28 minutes 49 seconds training time consumption. This proposed model is tested with reference speed step response and parameters uncertainties. The result indicates that the proposed estimator improves voltage model and current model rotor flux observers for parameters uncertainties.

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Journal Info

Abbrev

IJPEDS

Publisher

Subject

Control & Systems Engineering Electrical & Electronics Engineering

Description

International Journal of Power Electronics and Drive Systems (IJPEDS, ISSN: 2088-8694, a SCOPUS indexed Journal) is the official publication of the Institute of Advanced Engineering and Science (IAES). The scope of the journal includes all issues in the field of Power Electronics and drive systems. ...