Research On Design And Fault Diagnosis Of Semi-direct Drive Permanent Magnet Synchronous Motor For Beam Pumping Units

Oil is an important energy source for national development.With the deepening of the fourth Industrial Revolution,the demand for crude oil is further rising.As the core of the pumping system,the performance of the driving motor of the pumping unit has an important effect on the efficiency of oil extraction.At present,the most commonly used motor in oil field is three-phase asynchronous motor,which cannot match the mechanical load characteristics of pumping unit well.Its system efficiency is not higher than 30%,causing serious energy waste,difficult to meet the needs of enterprises to save energy and reduce consumption.In this paper,taking the beam pumping machine drive as the application background and the semi-direct drive permanent magnet synchronous motor as the research object,the matching of the suspension point dynamogram of the pumping machine and the torque characteristics of the motor is studied.A high-efficiency tangential ferrite permanent magnet synchronous motor is proposed,which can replace the original three-phase asynchronous motor and its pulley deceleration system for pumping machines.The structure of hybrid excitation power adjustable motor is proposed,and the output torque covers the needs of multi-model pumping machines.The fault diagnosis method of pumping machine is studied,and the fault diagnosis method based on torque work diagram is proposed to realize intelligent fault diagnosis.The main work of this article is as follows:Aiming at the problem that the characteristics of the pumping machine and the motor cannot be well matched,a motor torque work diagram based on the dynamogram is proposed for the study of characteristic matching.The basic structure of the permanent magnet synchronous motor is analyzed,and a 24 k W tangential ferrite permanent magnet synchronous motor is designed based on the parameter requirements of characteristic matching,and its electromagnetic characteristics and magnetic loss characteristics are analyzed.Its superior performance can replace the original 37 k W three-phase asynchronous motor,greatly saving electric energy.The performance and cost differences between Nd Fe B motors and ferrite motors are compared,and the performance advantages and price advantages of this design are analyzed.The experimental prototype was made and tested in the oil field,the test results of the prototype were consistent with the simulation,and the high efficiency was still high under light load.The load and temperature rise experiments verified the design effectiveness and could meet the working requirements of the pumping machine.The defects of cogging torque and torque fluctuation of tangential ferrite permanent magnet synchronous motor are analyzed,and a multi-objective optimization function is proposed to optimize the motor.By optimizing the stator structure,rotor structure and permanent magnet size,while ensuring the output torque,the amount of permanent magnet is reduced,and the torque ripple and cogging torque are weakened.A power adjustable motor structure is proposed,and the performance of the motor under different mixing methods and different material content of magnet is studied.Without changing the base number,the power can be adjusted by changing the ratio of Nd Fe B and ferrite to meet the performance needs of different types of pumping machines.Aiming at the problem of low intelligence in fault diagnosis of pumping machine,a method for fault diagnosis based on motor torque work diagram is proposed.A sample library of torque work diagrams under common fault conditions is established,and a differential curve of torque work diagrams is proposed.The characteristic parameters of the torque work diagram and its differential curve were extracted,and a fault diagnosis model was built based on the artificial neural network.The diagnostic model is optimized through noise training and MIV evaluation.After testing,the diagnostic model has a high accuracy rate,which can be used for intelligent diagnosis of oil wells,which is conducive to the digital management of oilfields.