FCS-MPC Based Pre-Filtering Stage for Computational Efficiency in a Flying Capacitor Converter

Cristian Garcia,Mohammad Mohammadinodoushan,Venkata Yaramasu,Jose Rodriguez,S Alireza Davari,Zhenbin Zhang,Davood Arab Khaburi,Margarita Norambuena

doi:10.1109/access.2021.3103070

Abstract

Finite Control Set Model Predictive Control (FCS-MPC), thanks to its simple and powerful concept, has been applied widely to control the multilevel converters. FCS-MPC allows the control of several objectives and can incorporate restrictions, even nonlinear, into the mathematical model of the system under consideration. However, FCS-MPC needs to evaluate all the switching states of the multilevel converter to find the best switching state to be applied in the next sampling time. For higher-level multilevel converters, this iterative action requires computational capacity that is far beyond the digital controller’s capacity in the current market. This paper proposes a new predictive geometric pre-filtering strategy to reduce the iterations and computational burden without affecting the dynamic performance of FCS-MPC. This method consists of a novel pre-filtering stage that uses the predictive model of the system and geometrical properties to find the sector where the reference vector is located and evaluates few vectors that constitute the optimal sector. The proposed method is experimentally validated using a four-level three-cell flying capacitor converter with 512 voltage vectors, obtaining a 64% reduction in the computational burden, while achieving excellent electrical performance indices and maintains the high dynamic performance of the standard FCS-MPC.

Highlights

F INITE Control Set Model Predictive Control (FCSMPC) has been proven, in recent years, as an attractive alternative for controlling power converters in various energy conversion applications [1], [2]
Given that in Finite Control Set Model Predictive Control (FCS-MPC), the system performance should be evaluated by a cost function for all possible switching states to apply the best one to the converter in the sampling time, the higher number of states lead to high computational burden
Several other approaches are presented to reduce computational burden of FCS-MPC: 1) use of switching tables based on control objectives to reduce the number of vectors that should be evaluated [17], 2) use of mathematical transformations to obtain an equivalent optimization of the problem even for long prediction horizon [18], 3) consideration of electrical behavior of each switching state and set an error tolerance to consider only some switching states in the evaluation of the cost function [19], 4) use of the dead-beat technique to reduce the number of candidates to be evaluated in the cost function [20]–[22]

Summary

INTRODUCTION

F INITE Control Set Model Predictive Control (FCSMPC) has been proven, in recent years, as an attractive alternative for controlling power converters in various energy conversion applications [1], [2]. Several other approaches are presented to reduce computational burden of FCS-MPC: 1) use of switching tables based on control objectives to reduce the number of vectors that should be evaluated [17], 2) use of mathematical transformations to obtain an equivalent optimization of the problem even for long prediction horizon [18], 3) consideration of electrical behavior of each switching state and set an error tolerance to consider only some switching states in the evaluation of the cost function [19], 4) use of the dead-beat technique to reduce the number of candidates to be evaluated in the cost function [20]–[22] All these methods reduce the computational burden, with some sacrifice in the steady-state and dynamic performance of the converter. This paper proposes a predictive control strategy with geometric pre-filtering algorithm that divides the space vector of the multilevel converter into six sectors and detects where the reference vector is located This stage is carried out before the cost function evaluation to reduce the switching states that should be considered in the online optimization. The steady-state and transient performance of the proposed method is assessed and compared to the standard FCS-MPC method

MATHEMATICAL MODEL

MODEL PREDICTIVE CONTROL

IMPLEMENTATION

EXPERIMENTAL RESULTS

CONCLUSIONS