The energy harvested by an ocean wave energy converter (WEC) can be enhanced by a well-designed wave-by-wave control strategy. One of such superior control methods is model predictive control (MPC), which is a nonlinear constrained optimization control strategy. A limitation of the classical MPC algorithm is its requirement of an accurate WEC dynamic model for real-time implementation. This article overcomes this challenge by proposing a data-driven MPC scheme for wave energy converters. The data-based WEC model is developed by a Gaussian process (encompassing mean predictions and symmetric uncertainties) for a more accurate description of nonlinear and unmodeled system dynamics. A cross-entropy solver for data-driven MPC is employed for rapid, high-performance results, which samples trajectories from Gaussian distributions based on the concept of the symmetry principle. The proposed strategy is verified numerically by simulations which demonstrate its superior performance over a classical complex-conjugate controller.

Modular multilevel converters (MMCs) have gained widespread adoption in high-voltage direct current (HVDC) transmission due to their high voltage levels, low harmonic content, and high scalability. However, conventional control methods such as finite control set model predictive control (FCS-MPC) suffer from a heavy computational burden and sensitivity to system parameter variations, limiting the performance of MMCs. This paper proposes a data-driven approach based on model-free adaptive control with an event-triggered mechanism that demonstrates superior robustness against parameter mismatches and enhanced dynamic performance in response to sudden output changes. Moreover, the introduction of the event-triggered mechanism effectively reduces redundant operations, decreasing the computational burden and switching losses. Finally, the proposed strategy is validated through a MATLAB/Simulink simulation model.

Under overmodulation conditions, the capacitor voltages of half-bridge and full-bridge submodules in hybrid modular multilevel converters (MMCs) may become unbalanced. This imbalance not only gives rise to overvoltage stress on submodule capacitors, jeopardizing equipment safety, but also degrades power quality and may even trigger operational instability. To address this issue, this paper proposes a minimum second harmonic circulating current injection method based on Bayesian Adaptive Direct Search (BADS) within the overall framework of model predictive control for MMCs. The method efficiently solves complex objective functions by alternately performing local Bayesian optimization and global grid search. Optimal second harmonic injection values under different modulation indices are obtained through offline computation and curve fitting. This approach achieves dynamic capacitor voltage balancing across a wide modulation range while minimizing operational losses caused by harmonic currents.