Sensorless Speed Estimation for Line-connected Induction Motor Based on Recurrent Multilayer Neural Network
A new method for rotor sensorless speed estimation of line-connected induction machines is proposed in this paper. Considering that it is difficult to install speed sensor for submersible induction motor in some specific situations such as special high temperature working environment, the proposed technique of recurrent multilayer neural network is used to estimate the speed of sensorless submersible induction motor. The stator current measured by data collector is analyzed by wavelet, thus the influence of high frequency noisy caused by high temperature is filtered off. Since the useful signal is extracted as sample input, and the speed signal collected by speed sensor as sample output, a neural network is trained on the principle of “training off-line, estimating on-line , so that the network can estimate the speed only using stator current. Experimental results prove that the proposed method has very high precision and good dynamic quality. Furthermore, the estimation results can provide powerful security for closed-loop control and fault diagnosis.
submersible motor sensorless speed estimation recurrent multilayer neural network wavelet analysis
Jing Yang Liguo Wang Dianguo Xu Bing Xue
Department of Electrical Engineering Harbin Institute of Technology Harbin 150001 Heilongjiang Province, China
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）