Method to Enable Reduced Sensor Capacitor Voltage Estimation in Modular Multilevel Converters

Abstract

Bulk power applications such as shipping increasingly consider multilevel converter topologies such as modular multilevel converters (MMC), which offers the advantages of scalability, good power quality, and reconfigurability. The internal functioning of MMC requires complete knowledge of the capacitor voltages that make up their submodules meaning a large number of sensors are needed and thus a high number of potential points of failure exist. To increase reliability and reduce investment costs, state estimation techniques such as KALMAN filters have been employed to replace the physical sensors. Analytical techniques based on the knowledge of arm current, arm voltage, and submodule states have also been developed. These techniques exploit the fact that at an insertion index of 1, the arm voltage equals the capacitor voltage on the submodule which permits the estimation algorithm to refresh periodically with measured data thereby increasing the accuracy. This method requires a long refresher time, especially when many submodules are used per arm. In this study, we propose an improved analytical estimation by not only using unity insertion indices, but also exploiting transitions between two successive insertion indices. The study was carried out on a 4 submodule per arm MMC system. The estimated capacitor voltages were then compared with sensor-based voltage measurements confirming the validity of the proposed method. It was then integrated into a complete MMC controller including the inner controls such as circulating current and capacitor voltage balancing.