Reduced Computational Burden Model Predictive Current Control of Asymmetric Stacked Multi-Level Inverter Based STATCOM

Abstract

Model predictive control (MPC) is one of the control strategies used recently to control power converters because of its advantages of handling nonlinear functions, fast dynamic response, and eliminating the modulators and PI regulators. However, it has one disadvantage: it requires high processing power to operate correctly due to the high computational burden on the controller. This paper presents a new method of controlling the performance of grid-tied multilevel inverter functioned as STATCOM based on finite control set model predictive current control (FCS-MPCC). This control strategy is employed to control a five-level asymmetric stacked multilevel inverter (ASMLI) and coupled with the network through LCL-filter. Multilevel inverters suffer from capacitors voltages unbalance problem, and LCL-filters require an efficient damping method to function appropriately; these two challenges are included in the cost function of the FCS-MPCC. The FCS-MPCC strategy is constructed in two methods. The first method is the traditional method, which requires 729 calculations per sampling instant. An effective calculation method has been proposed as a second method, as it requires 27 calculations at each sampling instant. These two methods are compared to ensure that the proposed method does not affect STATCOM performance. MATLAB / Simulink is used to simulate these two models. The result demonstrates that the proposed method reduces implementation time five times than the other method with no loss of reference traceability, balanced capacitors voltage, and damped filter performance.