NDLI: A novel integration algorithm for flux and torque estimation of induction motor in low speed operation

Content Provider	IEEE Xplore Digital Library
Author	Guangbin Ding Peilin Pang
Copyright Year	2008
Description	Author affiliation: Hebei Univ. of Eng., Handan (Guangbin Ding; Peilin Pang)
Abstract	A novel method of stator resistance identification based on wavelet network is presented and the determination of wavelet network structure is discussed in order to improve the low-speed dynamic performance of induction motor in direct torque control. The current error and the change in the current error is the inputs of the wavelet network and the stator resistance error is the output of the wavelet network. The network structure and parameter identification is fulfilled by the evolutionary algorithm. The characteristics of a signal both in the time and frequency domains are localized accurately by means of wavelet transform, so the occurring instants of the stator resistance change can be identified by the multi-scale representation of the signal. The accurate stator flux vector and electromagnetic torque are acquired by the parameter estimator once the instants are detected, in this way the direct torque control can be applicable in the low region and the inverter control strategy can be optimized. The simulation results show that the wavelet-based method can efficiently reduce the torque ripple and current ripple and is superior to the BP neural network.
Starting Page	226
Ending Page	229
File Size	104179
Page Count	4
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605075
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Resistance Training Torque Dynamic system Wavelet network Torque control Induction machine Artificial neural networks Stators Approximation methods Control strategy Resistance identification
Content Type	Text
Resource Type	Article

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