In this work, we develop a zero-sequence model to analyze the zero-sequence current, i.e., circulating current, in multiple paralleled power converters. On this basis, we propose and verify a novel and simple solution: predictive zero-sequence current control (PZSC $^2$ ) to freely mitigate the zero-sequence current. The proposed technique is implemented in a direct model predictive control framework via a synthesized voltage vector, which consists of an optimal shrunken voltage vector and a zero voltage vector. The former is selected to minimize the tracking error of the $\alpha \beta$ -axis current, while the latter is properly selected to mitigate the circulating current. Notably, the zero voltage vector is solely determined by the zero-sequence current direction, without using other converters’ operating knowledge. This permits the proposed PZSC $^2$ to operate in a self-governing manner, fitting the spatially distributed applications. We validate the proposed solution through a lab-constructed hardware test bench. Experimental results verify its effectiveness considering both steady-state and transient performance.

In offshore wind farms connected via diode-rectifier-based high-voltage dc, conventional distributed control strategies have significant coupling among active and reactive power, leading to large oscillations in wind turbine (WT) converter power, voltage, and grid frequency during transients. In this work, we propose a decoupled distributed control strategy based on a novel adaptive virtual impedance (VI) method. In detail, we newly propose a simple estimator to enable each WT controller to locally estimate the global average reactive power, without the need for communication and line impedance knowledge. By comparing the estimated global average value with the actual reactive power, we achieve dynamically adjustable VI, thereby completely decoupling the power control. In addition, a proportional feedforward control is integrated into the estimator, ensuring precise reactive power-sharing among WT converters. We also develop a small-signal model of the proposed method to theoretically confirm the decoupling characteristics, and to facilitate system stability analysis and key control parameters design. Experimental results validate the superior performance of power and frequency regulation during both steady and transient states, in comparison with the existing decoupling control method.

Modular multilevel converters (MMC) are widely used in voltage-sourced converter-based high-voltage dc due to their modular design, ease of scale, and fault tolerance capabilities. The multivariable control objectives of MMCs can be achieved by model predictive control (MPC) due to MPC's fast dynamic response and ease of implementation. Nonetheless, conventional MPC techniques for MMC have shortcomings, including high computational requirements and poor circulating current suppression. First, this study proposes an adaptive fast-optimization MPC that adapts the number of candidate combinations to the steady and transient states, significantly reducing the computational burden. Second, an improved hybrid combination of MPC with proportional resonance controller is used to enhance the circulating current suppression performance. Simulations and experimental case studies on a three-phase grid-connected MMC validate the proposed techniques.

Direct model predictive control (DMPC) effectively integrates multiobjective control and modulation processes into a single stage while handling constraints. However, the classical DMPC method faces computational challenges in cascaded H-bridge static var generator (CHB-SVG) due to the exponential growth of finite control sets. This work proposes an efficient DMPC method for CHB-SVG constructed by three stages: current tracking, phase voltage balancing, and module voltage balancing. The current tracking optimization stage is reformulated as Euclidean distance minimization and solved in a 120${}^{\circ}$ coordinate system. Analytical solution for optimal voltage levels is derived to ensure phase voltages equilibrium. Complementary switching states are introduced within the module voltage balancing stage, improving voltages balancing performance. This approach effectively reduces the complexity of the current and phase voltage balance control from untraceable polynomial time to a constant executable time. Both simulation and experimental results confirm the effectiveness of the proposed method.

During asymmetrical grid faults, the control of the doubly fed induction generator (DFIG) must simultaneously consider both grid code requirements and the system’s intrinsic constraints. This work proposes an asymmetrical grid fault ride-through control strategy for DFIG simultaneously considering dc-link voltage limitation, positive- and negative-sequence reactive current requirements, and electromagnetic torque oscillations. The method effectively mitigates RSC over-modulation imposed by the dc-link voltage limit and maintains control of the DFIG during asymmetrical grid faults while providing reactive current support. By fully utilizing the dc-link voltage, electromagnetic torque oscillations are minimized to the greatest extent possible. Meanwhile, the range of operating conditions within which the reactive current requirements can be satisfied is significantly expanded. The effectiveness of the proposed strategy is verified by both simulations and experimental data.

Multi-step predictive control will significantly reduce the switching frequency of a power conversion system at the same current quality, which is suitable for high power offshore wind turbine systems. However, the computational burden increases exponentially with the prediction horizon, making it difficult to apply in practical. In order to solve the above problems, a multi-step predictive control method based on ant colony optimization is proposed. The proposed method maintains good control performance and reduces the computational burden by 90% compared to traditional enumeration strategies. The innovations of the proposed method are: An ant colony optimization framework for back-to-back converter PMSG wind turbine systems with multi-step MPC is constructed, and a pheromone update mechanism is designed to fit the system. The relevant parameters design is also introduced.