Research On Model Predictive Direct Power Control Of Brushless Double-fed Generator

With the proposal of the "carbon peaking and carbon neutrality" goal,the country continues to attach importance to the development and utilization of clean energy.Wind energy is an important part of clean energy.At present,wind turbines mainly include permanent magnet synchronous generators and doubly-fed generators.Permanent magnet synchronous generators are mainly used in offshore wind power,which have problems such as high device requirements,high motor costs,and high operating losses.And the doubly-fed motor is mainly used in onshore wind power.This type of motor contains brushes and slip rings,which have the problems of high cost and low reliability.It leads to frequent maintenance in the later period and affects the stability of the power generation system.Compared with the double-fed motor,the brushless doubly-fed motor eliminates the need for brushes and slip rings,which can reduce the failure rate and improve the operational reliability.Therefore,brushless doubly-fed generators have gradually become a research hotspot at home and abroad.This thesis takes the brushless doublyfed wind turbine as the research object,which realizes the optimization of the direct power control performance through the control strategy of expanded state observation explicit model predictive control and particle swarm parameter optimization.First of all,this thesis is based on the structure and control strategy of the brushless doublyfed motor,which expounds the basic principle of variable-speed constant-frequency power generation of brushless doubly-fed motor.And this thesis analyzes the mathematical model of the brushless doubly-fed motor,which builds the corresponding simulation model based on the mathematical model.According to the instantaneous power theory,the direct power mathematical model of the brushless doubly-fed motor is derived by analyzing the power flow of the open-winding brushless doubly-fed motor in different operating states.Secondly,according to the mathematical model of direct power control,model-based predictive direct power control is proposed and applied to open-winding brushless doubly-fed machines in this thesis.However,the traditional model predictive control is limited by the online computing power.It is only suitable for the industrial process control field where the dynamic response speed is not high,but not suitable for large-scale wind power generation systems.In order to improve the dynamic response characteristics of traditional model prediction,this thesis proposes an explicit model prediction direct power control strategy for open-winding brushless doubly-fed machines.This control strategy selects the optimal control quantity through offline calculation and online search,which enables real-time control of power.The error compensation is not considered in the above-mentioned model predictive control,which will result in insufficient control accuracy of the system.Therefore,the extended state observer is used for error compensation,which improves the control accuracy of the system.Then it is compared with the traditional model predictive direct power control,and the two control algorithms are simulated and analyzed under different operating conditions.At last,the proposed control strategy is verified by a hardware-in-the-loop experimental platform.The experimental results show that the proposed model predictive control strategy improves the computational efficiency and response speed of the system.And the proposed control strategy can enhance the stability and the control precision of the system.Finally,in order to solve the problem of complex parameter setting in traditional active disturbance rejection controller direct power control,the particle swarm parameter optimization algorithm and active disturbance rejection controller were combined and applied to openwinding brushless doubly-fed motor for direct power control.The direct power control strategy of particle swarm active disturbance rejection controller is verified by simulation and experiment.The results show that the improved control strategy proposed in this thesis can improve the tuning ability of the parameters of the active disturbance rejection controller compared with the traditional active disturbance rejection controller.The control strategy finally achieves the improvement of system stability and control accuracy by selecting better tuning parameters in this thesis.In this thesis,the direct power control of the open-winding brushless doubly-fed motor is carried out by using the expanded state observation explicit model predictive control strategy and the particle swarm parameter optimization active disturbance rejection controller.Through simulation and experimental analysis,it is verified that the proposed control strategy can improve the dynamic response capability and control accuracy of the system.It can enhance the stability of the system and lay the foundation for the wide application of brushless doublyfed motors in the industrial field.