摘要
鉴于潜油电机独特的高温工作环境所导致传感器安装困难的不足,该文利用Elman神经网络对无速度传感器潜油电机进行了速度辨识。实验方案中把数据采集卡采集到定子电流用小波分析,滤除高温所产生的高频噪声的影响,提取有用的信号作为样本输入,把测速发电机采集到的速度信号作为样本输出,按照"离线训练,在线辨识"的思想训练神经网络,使之仅通过定子电流就能对潜油电机速度进行辨识,实验证明本系统具有很高的稳态精度和良好的动态性能,其辨识结果可为进一步实现潜油电机的闭环控制和故障诊断提供有力保障。
In consideration of the difficulty to install speed sensor result form special high temperature working environment of submersible motor, in this paper, a method of Elman neural network is used to estimate the speed of sensorless submersible motor. In the experiment, the stator current measured by data collector was analyzed by wavelet, thus the influence of high frequency noisy caused by high temperature is filtered off, and the useful signal is extracted as sample input, the speed signal collected by speed sensor as sample output, a neural network is trained on the principle ＂training off-line, estimating on-line ＂, so that the network can estimate the speed only using stator current. It is proved to have very high precision and good dynamic quality. Furthermore, the estimation result can provide powerful security for closed-loop control and fault diagnosis.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2007年第24期102-106,共5页
Proceedings of the CSEE
