In this work, an idea based on the bisection algorithm is used to reduce the computational burden of indirect finite control set model predictive control (FCS-MPC) for modular multilevel converters (MMCs). The proposed method greatly reduces the search space for reaching the optimal insertion index (number of submodules to be inserted). Therefore, the strategy proposed offers similar steady-state and dynamic performance compared to full indirect FCS-MPC at a much lower computational burden. A new cost function is also proposed for indirect FCS-MPC which eliminates the need for an outer loop or additional control of differential current to regulate the summation voltages in each arm. The results of the proposed strategy are validated through simulations in MATLAB/Simulink.
In this paper, backstepping is applied as a first step of modulation control in the abc reference frame for modular multilevel converters (MMCs). In the second step, reduced indirect FCS-MPC is applied where the number of inserted modules are allowed to change by maximum one from the rounded result of the continuous outcome from backstepping. The backstepping method uses the ac-side current, differential current and summation of capacitor voltages in one arm as the state variables to form the Lyapunov functions. An established bilinear model of MMCs is used in the proposed design. The proposed approach offers similar dynamic performance as the full indirect FCS-MPC, at a much lower computational burden. The performance of the proposed method is validated by simulation.
In this paper, non-linear model predictive control (NMPC) without an explicit modulator is applied to modular multilevel converters (MMCs) in the abc reference frame. NMPC can easily be extended for longer prediction horizons as opposed to finite control set model predictive control (FCS-MPC). However, NMPC applied to power converters in previous studies uses a modulator, which limits the transient response compared to FCS-MPC. Therefore, to avoid the modulator, two strategies are presented. In the first strategy, the continuous solution (number of inserted submodules per arm) obtained from NMPC is simply rounded off to the nearest integer for both the arms of each phase. In the second strategy, the optimal solution obtained from the NMPC is further evaluated by rounding it up and down for both arms. This requires four simulations per time step, independently from the number of SMs per arm. The evaluation of the four cases is conducted only for the initial time step within the prediction horizon. Then the solution that minimizes a pre-defined cost function is applied to MMC. The second strategy offers the fastest response and provides similar dynamic performance as indirect FCS-MPC, while both strategies offer similar steady-sate performance. Simulations are performed to validate the performance of the proposed methods compared to the FCS-MPC.
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