Model predictive control based capacitor voltage balancing strategies for modular multilevel converters

Abstract

This paper presents two capacitor voltage balancing (CVB) strategies for modular multilevel converter (MMC) applications. Both balancing schemes are based on model predictive control and are designed to efficiently solve a constrained optimal control problem, where the predicted capacitor voltage errors are included in the cost function with the demanded output voltage of a cluster being forced through an equality constraint. The first method proposed in this paper computes specific modulation indexes for each module using the explicit solution of a relaxed version of the original optimization problem. The second approach proposed in this paper reduces the complexity of the original problem by linearizing the objective function and using an optimal sorting network based on a greedy algorithm to solve this approximation. Considering the structures of both solution approaches, they are integrated into modulation schemes based on phase-shifted and level-shifted pulsewidth modulation algorithms, respectively. Experimental results obtained from a nine-cell single-phase MMC prototype demonstrate the good performance achieved with the proposed methodologies, as well as the implementation simplicity offered by the proposed CVB algorithms.