摘要
在螺杆泵的采油过程中,因受到多方面因素的影响,而不能对转子的转速进行准确、实时的调整,为了解决这一问题,提出了一种基于改进BP神经网络的螺杆泵转速预置模型。首先,利用基于L-M算法的BP神经网络方法,预测了螺杆泵当前条件下的最优转速;然后,将所预测的螺杆泵最优转速传递给转速调整模块;最后,通过PID控制调节电机转速,构成了转速预置模型,该模型可以根据实时测得的原油温度、原油黏度、泵端压差、容积效率等数据来预置该条件下螺杆泵的最优转速。仿真实验结果表明:该模型对螺杆泵各工况下的最优转速预测平均相对误差为0.96%。研究结果表明:该模型对螺杆泵转速实时预置具有较好的效果,可为潜油螺杆泵采油系统中转速的实时调整打下基础,有利于提高螺杆泵的使用效率和经济使用寿命。
The rotor speed could not be adjusted accurately in real time,due to the rotor speed was affected by many factors in the process of progressing cavity pump production.In order to solve the problem,a presetting model of screw pump speed based on BP neural network was proposed.Firstly,the BP neural network method based on L-M algorithm was used to predict the optimal speed of the progressing cavity pump under the current conditions.And the predicted optimal speed was transferred to the speed adjustment module.Finally,the motor speed was adjusted by PID control to form the preset model of the speed,which could preset the optimal speed of the screw pump based on the real-time measured crude oil temperature,crude oil viscosity,pump end pressure difference and volumetric efficiency.The simulation experiment results show that the model has an average relative error of 0.96%in predicting the optimal speed of the screw pump under various working conditions.The results show that the model has a good effect on the real-time preset of the speed,which lays a good foundation for the real-time adjustment of the speed in the submersible screw pump oil extraction system.The basis of this is conducive to improving the efficiency and economic life of the screw pump.
作者
梁辉
王世杰
钱程
LIANG Hui;WANG Shi-jie;QIAN Cheng(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,China)
出处
《机电工程》
CAS
北大核心
2021年第9期1197-1201,共5页
Journal of Mechanical & Electrical Engineering
基金
中央引导地方专项资金资助项目(2020JH6/10500016)。
