Wang Hong-hua

Research on Reduction of Commutation Torque Ripple in Brushless DC Motor Drives Based on Fuzzy Logic Control

The increase use of variable-speed drive motors to reduce energy consumption will require a shift from PID controllers to system based on fuzzy logic algorithms to simplify design, reduce development time, and eliminate complex math formulas. This paper presents an approach for minimizing torque ripple in Brushless DC motor (BLDCM) due to phase commutation using fuzzy control. The BLDCM drive system based on fuzzy logic control uses double close loops with current hysteresis and speed fuzzy logic control. The traditional speed PID controller is replaced by fuzzy PI-like controller to improve the performance of the BLDCM drive system. The duty ratio is controlled by a “ deadbeat” controller to equalize the current slopes of the incoming and the outgoing phases of BLDCM during the commutation interval. The simulation results with MATLAB show the accomplishment of suppression of the spikes on the current and torque responses during the commutation intervals.

Research on fault diagnosis method of high-voltage circuit breaker based on fuzzy neural network data fusion

High-voltage circuit breakers are important electrical equipments which play the role of protection and control in the power network. In order to make the power system operate in a stable and reliable way, it is of great significance to make online fault diagnosis. This paper introduced the method of feature level data fusion using fuzzy neural network, and applied it into the fault diagnosis of high-voltage breakers. The results of the diagnosis showed that the model had good fault identification capability, it could deal very well with both the uncertain knowledge and ambiguous data, as well as improve the accuracy of fault diagnosis.

Intelligent Sensorless Control of Permanent Magnet Synchronous Motor Drive

After analyzing the basic principle of sensorless control using extended Kalman filter (EKF) in permanent magnet synchronous motor (PMSM) drive, this paper proposes an intelligent sensorless control. Here, fuzzy control is used in EKF and neuron control is used in speed loop, since neuron based speed controller does not rely on the accurate mathematical model of system, and it has not only fast dynamic response but also high steady-state accuracy, while fuzzy EKF algorithm can adjust covariance matrices online and be efficiently-accelerated convergence. A PMSM drive simulation model with novel intelligent sensorless control is created and studied using Matlab/Simulink. The simulation results demonstrate the feasibility and validity of novel intelligent sensorless control.

Optimization of interval time of two stage commutation method for the reduction of vibration and acoustic noise in SR motors

This paper describes an investigation into the limitation and improvement of the two stage commutation method to minimize stator vibration and acoustic noise in SRM .The study shows the interval time between two stage of commutation process should be optimized to reduce the stator vibration more effectively without affecting the performance of the SRD. Several particular principles for optimization of interval time of two stage commutation method for restrain overall vibration in SRM are outlined. Experimental results based on a laboratory prototype demonstrate the effectiveness of the proposed principles.