Romeral, L.; Salehi Arashloo, R.; Salehifar, M.; Moreno-Eguilaz, J.M. Electric power systems research Vol. 121, p. 260-269 DOI: 10.1016/j.epsr.2014.11.004 Data de publicació: 2015-04-01 Article en revista
Model predictive control algorithms have recently gained more importance in the field of wind power generators. One of the important categories of model predictive control methods is improved deadbeat control in which the reverse model of generator is used to calculate the appropriate inputs for the next iteration of controlling process. In this paper, a new improved deadbeat algorithm is proposed to control the stator currents of an outer-rotor five-phase BLDC generator. Extended Kalman filter is used in the estimation step of proposed method, and generator equations are used to calculate the appropriate voltages for the next modulation period. Two aspects of proposed controlling method are evaluated including its sensitivity to generator parameter variations and its speed in following the reference values of required torque during transient states. Wind power generators are kept in mind, and proposed controlling method is both simulated and experimentally evaluated on an outer-rotor five-phase BLDC generator. (C) 2014 Elsevier B.V. All rights reserved.
This paper develops and analyzes a methodology for detecting stator winding inter-turn faults in surface-mounted permanent magnet synchronous motors. The proposed methodology is based on monitoring the zero-sequence voltage component having into account the effects of the inverter that usually feeds the machine. The theoretical basis of such a method is established from the parametric model of the machine. Attributes of the method presented here include simplicity, high accuracy, low computational burden and high sensibility. Additionally, it is especially useful when dealing with fault tolerant systems. From this model the expression of the zero sequence voltage component is deduced, which is used to detect stator winding inter-turn faults. Both simulation and experimental results presented in this work show the potential of the proposed method to provide helpful and reliable data to carry out an online diagnosis of such faults.
Permanent magnet synchronous motors (PMSMs) are applied in high performance positioning and variable
speed applications because of their enhanced features with respect to other AC motor types. Fault
detection and diagnosis of electrical motors for critical applications is an active field of research. However,
much research remains to be done in the field of PMSM demagnetization faults, especially when running
under non-stationary conditions. This paper presents a time–frequency method specifically focused to
detect and diagnose demagnetization faults in PMSMs running under non-stationary speed conditions,
based on the Hilbert Huang transform. The effectiveness of the proposed method is proven by means of