摘要
抽油机所用电动机的输出扭矩是分析抽油机动态平衡的重要参数。针对传统测量方法存在成本高、安装困难、可靠性差等不足,提出了基于抽油机系统数学模型和BP神经网络模型这2种软测量扭矩的方法。研究结果表明,这2种方法克服了传统测量方法的不足,并获得了较高的测量精度。由于这2种方法具有各自的特点,因此,可以应用于不同要求的抽油机扭矩测量系统中。
The output torque of the motor used in oil pumping unit is an important parameter for analyzing dynamic balance of the oil pumping unit. Aiming at the disadvantages of traditional measurement method, such as high cost, difficult installation, and poor reliability, etc. , two of the soft sensing methods based on mathematical model of the oil pumping system and BP neural network are proposed. The results of research indicate that these two methods overcome the disadvantages of traditional measuring methods, and offer higher measurement accuracy. These two methods possess their own features, thus they can be used in torque measuring systems with different requirements.
出处
《自动化仪表》
CAS
北大核心
2009年第9期21-23,共3页
Process Automation Instrumentation
关键词
扭矩
软测量
抽油机
数学模型
BP神经网络
Torque Soft sensing Oil pumping unit Mathematical model BP neural network