Principles and latest progress of Doubly Fed Brushless Machine (DFBM) are reviewed with the identified solutions to designing a high efficiency DFBM of 96%. The original design of DFBM rated at 200kw/1200rpm focuses on the key components of DFBM, including the rotor style and pole number combination, and control algorithms. Both theoretical and experimental results are presented to substantiate the success of the original design solutions.Keywords-doubly fed, brushless, high efficiency, decouple control of active and reactive power, wind power generator

An aircraft's vertical fin may experience dramatic buffet loads in high angle of attack flight conditions, and these buffet loads would cause huge vibration and dynamic stress on the vertical fin structure. To reduce the dynamic vibration of the vertical fin structure, macro fiber composite (MFC) actuators were used in this paper. The drive moment equations and sensing voltage equations of the MFC actuators were developed. Finite element analysis models based on three kinds of models of simplified vertical fin structures with surface-bonded MFC actuators were established in ABAQUS. The equivalent damping ratio of the structure was employed in finite element analysis, in order to measure the effectiveness of vibration control. Further, an open-loop test for the active vibration control system of the vertical fin with MFC actuators was designed and developed. The experimental results validated the effectiveness of the MFC actuators as well as the developed methodology.

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The fast and stable inner current loop in the permanent magnet synchronous motor control system is the key factor that ensures the torque control performance of the motor. The deadbeat predictive current control has good dynamic response performance, but it depends heavily on the precise mathematical model of the controlled object. The parameter mismatch will degrade the control performance. A deadbeat predictive current control method based on online parameter identification is proposed in this study. This method does not need to inject additional d ‐axis current to identify the parameters during the operation of the motor; it only needs to make full use of the inherent phenomenon that the q ‐axis current changes when the load of the motor changes during operation, and perform parameter identification. Aiming at the problem that the effect of parameter identification is easily affected by motor speed, a new variable step‐size neural network algorithm is designed in this study. The speed factor is introduced into step function to ensure the performance of the identification algorithm at a different speed. Finally, based on the new online parameter identification algorithm, the deadbeat predictive current control method is used to verify the experiment.

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