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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.

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.

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A new method for adaptive ac filter parameters identification of three phase pulse-width modulated rectifiers is presented in this paper. The proposed method is based on three adaptive linear neurons to identify online the ac filter resistances and inductances. The main advantage of this method is its simplicity and it requires low computational cost. The identification algorithm is implemented and three experimental tests have been realized in order to identify in real time the ac filter resistances and inductances. First test is performed in steady state operation. Two other tests are conducted to testing the track ability of the proposed method relative to parameters variations. Experimental results demonstrate that the proposed method provides accurate ac filter parameter values and tracks well the parameters variations. The obtained parameter values can be exploited for diagnosis purposes of ac filter status or in a control strategies.