Abstract
Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach.
Highlights
The Modular Multilevel Converters (MMCs) topology was presented in [1]
We present the computational time of running the nonlinear Model predictive control (MPC) (NMPC) using the ACADO toolkit interface with MATLAB. Based on those results and the capacity of field-programmable gate array (FPGA) to outperform microprocessor implementations in terms of computational speed [24], the developed NMPC algorithm can be implemented in real time for much longer prediction horizons (i.e., Np = 25 up to Np = 100 compared to previous MPC solutions for MMCs surveyed above that use Np = 1 up to Np = 10)
A nonlinear model predictive control (NMPC) scheme for MMCs was proposed in this paper, which was motivated by the fact that linearization-based MPC for MMCs is limited to relatively short prediction horizons
Summary
The Modular Multilevel Converters (MMCs) topology was presented in [1]. This type of power electronics hardware has as its main feature a series connection of module capacitors called submodules (see Figure 1). Motivated by the above analysis and the advances in sequential quadratic programming (SQP) solvers for nonlinear MPC (NMPC) [22,23], in this paper, we develop a continuous-control-set long-horizon NMPC algorithm for current control in MMCs. The NMPC controller optimizes a cost function that penalizes the above-mentioned (i)–(iii) objectives subject to constraints. We present the computational time of running the NMPC using the ACADO toolkit interface with MATLAB Based on those results and the capacity of field-programmable gate array (FPGA) to outperform microprocessor implementations in terms of computational speed [24], the developed NMPC algorithm can be implemented in real time for much longer prediction horizons (i.e., Np = 25 up to Np = 100 compared to previous MPC solutions for MMCs surveyed above that use Np = 1 up to Np = 10). For a set S ⊆ Rn, define S[a,b] := {s ∈ S : a ≤ s ≤ b} and SN := S × . . . × S
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