Virtual Flux Estimation for Sensorless Predictive Control of PWM Rectifiers Under Unbalanced and Distorted Grid Conditions

In this article, a new neural network-based virtual flux (NN-VF) estimator is proposed for sensorless control of a pulsewidth modulation rectifier under unbalanced and distorted grid conditions. In this estimator, the VF vector is reconstructed through an emulated ideal integrator. Thereafter, positive and negative sequence (PNS) VF components are extracted using an NN-based PNS components separator. A Lyapunov's theory-based convergence analysis is performed for optimal tuning of the NN-VF estimator. Accordingly, accurate and fast estimation is achieved. A VF-based predictive direct power control (VF-PDPC) model, including the estimated VF positive sequence components, is formulated. A startup procedure is considered for smooth starting under unbalanced grid conditions. Feasibility and robustness of the developed VF-PDPC are verified by experiments. Mainly, obtained results demonstrate that the startup procedure reduces settling time and avoids over currents; the VF-PDPC achieves better current waveforms than those obtained by the conventional PDPC under unbalanced grid conditions; the NN-VF estimator presents similar dynamic performances as the second-order generalized integrator that uses grid voltages measurement.