Noumidia Amoura

This paper presents a grid-voltage-sensorless control scheme for a grid-connected modular multilevel converter based on an adaptive-linear-neuron quadrature signal generator for virtual-flux estimation (VF-ADALINE-QSG). The proposed estimator identifies the DC offset component and extracts the in-phase and quadrature fundamental components online without requiring offline training or specific initialization of the adaptive weights. By reconstructing VF exclusively from the identified fundamental components, the proposed method limits drift induced by DC offset and attenuates higher-order harmonics while avoiding the additional dynamic delays associated with conventional filtering-based estimators. The VF-ADALINE-QSG is integrated into a deadbeat predictive control framework incorporating grid-current regulation, circulating current suppression, and submodule-capacitor-voltage balancing. Its performance is compared to that of low-pass-filter (LPF) and second-order generalized-integrator (SOGI)-based VF estimators through real-time simulations on an OPAL-RT OP5700 platform. The evaluation considers startup, active-power-reference variations, a 20% DC offset, voltage distortion comprising 12.5% fifth- and 7.5% seventh-order harmonics, and the simultaneous occurrence of these disturbances. The proposed estimator achieves a settling time approximately one-sixteenth of that obtained with the LPF-based estimator. Under DC offset conditions, the estimated VF total harmonic distortion is limited to 0.26%, compared with 7.38% and 0.84% for the LPF- and SOGI-based estimators, respectively. The results demonstrate a faster transient response, improved estimation accuracy, and enhanced robustness against both DC offsets and harmonic disturbances.

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions.