No abstract

No abstract

No abstract

Two new methods to identify the mechanical parameters in induction motor based field oriented drives are presented in this paper. The identified parameters are: the moment of inertia and the viscous damping coefficient. The proposed methods are based on the adaptive linear neuron (ADALINE) networks. The two parameters are derived and optimised during the online training process. During the identification phase, the motor torque is controlled by the well-known field oriented control strategy. This torque is subjected to variations in order to obtain mechanical speed transients. The two proposed methods are simple to implement compared to the previous techniques. They require only the stator current and mechanical speed measurements. Finally, the effectiveness of the two methods and the accuracy of the derived parameters are proven experimentally by two direct starting tests. The originality of this work is the building of a model representation that it is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of mechanical parameters identification.

This paper proposes an adaptive voltage-oriented control (VOC) with online ac filter parameters identification for three-phase voltage-source rectifier (VSR). A new method based on adaptive linear neuron (ADALINE) is first designed to identify the ac filter parameters. For accurate identification, the VSR nonlinearity are included in the ADALINE structure. Thereafter, the developed ADALINE is inserted in the VOC to realize an adaptive VOC. Thus, the decoupled terms and the proportional-integral current controller gains are updated online. Finally, the ADALINE ability to track properly the ac filter parameters is investigated by experimental analysis. It shows that the VSR nonlinearity consideration has significant influence on the resistance identification. Compared to the VOC, the enhancement of the proposed adaptive VOC is experimentally proved. The originality of this paper is the building of a VSR model including VSR nonlinearity that is suitable for implementation with ADALINE. This leads to ease implementation and accurate identification.

No abstract

In this paper, an adaptive neural phase-locked loop (AN-PLL) based on adaptive linear neuron is proposed for grid-connected doubly fed induction generator (DFIG) synchronization. The proposed AN-PLL architecture comprises three stages, namely, the frequency of polluted and distorted grid voltages is tracked online; the grid voltages are filtered, and the voltage vector amplitude is detected; the phase angle is estimated. First, the AN-PLL architecture is implemented and applied to a real three-phase power supply. Thereafter, the performances and robustness of the new AN-PLL under voltage sag and two-phase faults are compared with those of conventional PLL. Finally, an application of the suggested AN-PLL in the grid-connected DFIG-decoupled control strategy is conducted. Experimental results prove the good performances of the new AN-PLL in grid-connected DFIG synchronization.

No abstract