Abstract
In the original model reference adaptive induction motor speed sensorless system based on flux linkage, there is a large fluctuation of the rotational speed in transient and steady state. When the motor speed is estimated, the integral part of voltage model affects the accuracy of the estimated speed with high-frequency signals and noise. In order to solve the above problems and further improve the system’s anti-interference performance and the speed estimation accuracy at low speed, an improved method of speed estimation that combines fuzzy proportional integral control and sliding mode control is proposed, by adopting genetic algorithm to optimize the parameters of the three sliding mode controllers, meanwhile, using the error integration criterion as the objective function of genetic algorithm optimization and searching for the optimal value of the objective function. Compared to the conventional method, the simulation results show the effectiveness of the proposed method in the middle- and low-speed regions with improved robustness against external disturbance, also display the high accuracy of estimated speed, the minor amplitude and frequency of speed fluctuation, and the great dynamic performance indexes of the system.
Highlights
Since the 20th century, with the increasing demand for electric power transmission, AC motor drive based on induction motor has attracted great attention
The motor speed is estimated based on the flux linkage model reference adaptive method
By the introduction of Butterworth filters and fuzzy proportional integral (PI) controllers, and according to Lyapunov’s stability theorem, sliding mode (SM) controllers are designed to replace the current regulators of traditional vector control system and PI regulator of sensorless speed system
Summary
Since the 20th century, with the increasing demand for electric power transmission, AC motor drive based on induction motor has attracted great attention. Extended Kalman filter is to make the linear system estimation algorithm apply to nonlinear systems, which deals with a series of problems caused by parameters variation and noise pollution effectively This computationally intensive method is time consuming, and there are many parameters needed to be adjusted. The steady-state reactive power including the rotational speed information is used as the adjustable model, and the adaptive law selects the proportional integral (PI) control. This paper makes improvement in accordance with the model-based reference adaptive induction motor speed sensorless system. Schauder use the voltage model as the reference and the current model as adjustable model to estimate the motor speed. In order to detect the anti-interference performance of the proposed method, the load torque is suddenly applied to the motor to observe the change of the speed. In order to solve this problem, the Butterworth filter is added to the system of speed estimation
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
