Research On Switched Reluctance Wind Power Generation Control System

As a new type of generator in the field of wind power generation,the switched reluctance generator has gradually become a research hotspot due to its advantages of simple structure,high reliability and easy maintenance.However,because of its special physical structure and difficult mathematical model,the control effect of the switched reluctance generator is difficult to improve.At present,the commonly control method of the switched reluctance generator is to control the parameters such as the switching angle,phase current and terminal voltage,but these methods have low control accuracy and poor output power quality.Therefore,in order to improve the quality and stability of the power output of the switched reluctance wind power generation system,this paper develops a control scheme of the optimal switching angle knowledge base and the double closed-loop control strategy based on iterative learning control.Then,the authors conducted in-depth exploration of the effectiveness and reliability of the control scheme.Switched reluctance generators are characterized by non-linearity,strong coupling,and numerous controllable parameters.This paper analyzes the relationship between the various parameters during its operation and establishes a mathematical model.According to the current change situation,it reveals the internal energy conversion process of the SRG.This paper analyzes and compares the advantages and disadvantages of the commonly used control methods,and selects the switching angle and phase current as the research direction of the control scheme.In order to improve the power generation quality of switched reluctance generators,this paper designs an optimal switching angle knowledge base based on differential evolution artificial bee colony optimization algorithm.According to the difference of the generator speed,the opening angle and the closing angle are dynamically adjusted so that the generator operates in the best state,effectively improves the generator's power generation capability.Aiming at the problem that the output voltage of the switched reluctance generator fluctuates severely,this paper uses the iterative control learning algorithm to efficiently track the desired trajectory,and proposes an iterative learning control algorithm based on voltage and current double closed-loop control method.The current-distribution strategy based on the minimum copper loss principle is used to determine the ideal trajectory of each phase current,and it is followed by an iterative learning controller to make the actual phase current track perfectly match the ideal trajectory.Then,combined with voltage feedback control can effectively suppress the fluctuation of the output voltage.For the control scheme proposed in this paper,this paper build a simulation platform for switched reluctance wind power control system.Simulation results show that the optimal switching angle knowledge base can effectively improve the power generation performance of the system,and ameliorate the torque ripple under the premise of maintaining high output power and energy conversion efficiency.At the same time,the simulation results also fully demonstrate that the double-closed-loop control system based on the improved iterative learning control has higher steady-state accuracy,faster response speed and good anti-disturbance capability,ensuring the system output is stable and reliable.