CONTROL SYSTEM OF WIND GENERATOR BASED ON SWITCHED RELUCTANCE MOTOR

2019 ◽  
Vol 2 (3) ◽  
pp. 230-242
Author(s):  
Ihor Kozakevych ◽  
Oleg Sinchuk ◽  
Denis Vornikov
Author(s):  
Long Chen ◽  
Haoxiang Wang ◽  
Xiaodong Sun ◽  
Yingfeng Cai ◽  
Ke Li ◽  
...  

A novel four-phase 16/10 belt-driven starter generator segmented switched reluctance motor has been proposed in a previous work to reduce torque ripple and increase the fault tolerance ability. Based on the previous research, the segmented switched reluctance motor digital control system is designed and presented. The digital control system including a power converter, detection circuits, and protection circuits is introduced in detail. For detection circuits, the half-detection method is employed to decrease the cost of the system. In addition, based on MicroAutoBox DS1401, a rapid control prototype platform is established. With this software system, it is easy to transfer control models and realize real-time control directly. Then, the speed closed closed-loop control for the segmented switched reluctance motor is applied to verify the proposed system. It contains current chopper control at a low speed and angle position control at a high speed. The simulation results are given, including the flux, current, torque, and efficiency range over the entire speed range of the segmented switched reluctance motor. Finally, the experimental results are presented to verify the simulation results and the effectiveness of the system. It can be found that the simulation and experimental results are consistent and acceptable, which means that the proposed digital system can operate naturally and accurately under speed closed loop control. Hence, the proposed digital system has high compatibility and practicability.


2012 ◽  
Vol 468-471 ◽  
pp. 2187-2192 ◽  
Author(s):  
Li Xiao ◽  
He Xu Sun ◽  
Feng Gao

Due to the shortcomings of long training time and slow convergence of BP neural network, this paper presents a new improved method that weight is no longer a constant but turned into a function of adjustable parameters. After the training of the improved BP neural network is completed, the network can map the nonlinear relationship between motor current, flux and rotor position. Based on the analysis of the unique structural properties of switched reluctance motor, this paper also proposes a method of greatly reducing the sample data to save computing time. Simulation results show that this method simplifies the complexity of the control system and improve detection accuracy, thus realize position sensorless detection of the switched reluctance motor.


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