The output current and voltage of grid-connected converters must comply with grid standards. This becomes particularly challenging under non-ideal grid conditions, where harmonics—introduced by other converters connected to the point of common coupling (PCC)—act as disturbances. Even though the profile of such harmonics may be known, suggesting the possibility of mitigation, variations in the impedance between these converters and the PCC result in disturbances with uncertain (but bounded) magnitude and phase. To deal with such uncertainties and fulfill grid standards while achieving the highest possible current quality, this paper proposes a robust harmonic-constrained optimization framework for computing optimized pulse patterns (OPPs). Numerical results demonstrate the superior performance of the proposed OPPs in meeting harmonic grid standards compared to established modulation methods, despite the disturbance profile being only partially known during the OPP computation process.

Model predictive control (MPC) has emerged as a promising approach for the control of systems with complex, non-linear dynamics, such as modular multilevel converters (MMCs). However, despite the intrinsic nonlinear nature of MMCs, linear MPC is predominantly applied, often without a thorough analysis of how the nonlinearities of the MMC affect control performance and constraint compliance. This paper identifies the impact of the MMC nonlinearities on the control performance of linear MPC and proposes a cascaded approach to mitigate their effects. The proposed approach ensures that the system constraints are still met when linear MPC is applied to the nonlinear MMC. The performance of the cascaded MPC strategy is validated by high-fidelity MATLAB/Simulink simulations and compared to state-of-the-art MPC implementations.

This paper addresses the current saturation challenge in modular multilevel cascade converters (MMCCs). The operation of the MMCC requires the control of ac, dc and circulating currents while accounting for limitations introduced by active or passive components such as semiconductor devices, capacitors, or inductors. Traditionally, current saturation methods focus primarily on limiting the ac currents, often neglecting the effect of their internal dynamics on the cluster currents. As a result, not all current limits are respected. This paper presents saturation methods that provide insight into the cross influences of the three current types, enabling simultaneous enforcement of all current limits. The proposed approaches ensure that the actual currents remain within the prescribed limits during all operating conditions. The effectiveness of the saturation method is validated through high-fidelity MATLAB/Simulink simulations of a high-voltage dc (HVDC) system based on MMCCs.

Six-phase permanent-magnet synchronous machines (PMSMs) offer multiple benefits compared with three-phase ones, e.g., increased torque density and reduced phase-current rating. A method has been recently proposed to generate current references ensuring minimum stator copper loss (SCL) per torque for nonsalient PMSM drives with sinusoidal or nonsinusoidal back-electromotive force (back-EMF), while the peak currents were suitably limited (with harmonics) to the maximum instantaneous currents of the inverter even during transient overloads. Furthermore, even greater mean torque could be attained by permitting a certain (limited) torque ripple when needed. However, such technique did not take into account the voltage limits imposed by the dc-link voltage. This paper proposes an alternative method based on look-up tables (LUTs) that provides these functionalities for symmetrical six-phase nonsalient PMSMs, while effectively considering the voltage constraints in the optimization. This makes it possible to minimize the losses and torque ripple per mean torque in not only the entire torque range, but also in the full speed range. To decrease the computational requirements, the offline LUT-generation process is designed so that it identifies the range of operating points where the solution changes, and then the optimization problem is solved only within such range.