Model-Free Predictive Current Control of a Voltage Source Inverter

Mahdi Shahparasti,Rasool Heydari,Jose Rodriguez,Freddy Flores-Bahamonde,Hector A. Young,Zahra Rafiee

doi:10.1109/access.2020.3039050

Mahdi Shahparasti, Rasool Heydari + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.3039050

Copy DOI

Abstract

Conventional model predictive control (MPC) of power converter has been widely applied to power inverters achieving high performance, fast dynamic response, and accurate transient control of power converter. However, the MPC strategy is highly reliant on the accuracy of the inverter model used for the controlled system. Consequently, a parameter or model mismatch between the plant and the controller leads to a sub-optimal performance of MPC. In this paper, a new strategy called model-free predictive control (MF-PC) is proposed to improve such problems. The presented approach is based on a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input (ARX) model. The proposed method provides an accurate prediction of the controlled variables without requiring detailed knowledge of the physical system. This new approach and is realized by employing a novel state space identification algorithm into the predictive control structure. The performance of the proposed model-free predictive control method is compared with conventional MPC. The simulation and experimental results show that the proposed method is totally robust against parameters and model changes compared with the conventional model based solutions.

Highlights

T HE high penetration of generators, loads and storage systems to the main grid, turns power converters and control architectures as main components for a more reliable operation
In order to validate the performance of the proposed modelfree predictive control structure, a simulated model in MATLAB/SimPowerSystems is compared with the conventional FCS-model predictive control (MPC) proposed in [7]
The reason is that the conventional Finite-Control-Set Model Based Predictive Control (FCS-MPC) needs to have the model of the system to accurately predict the output current of the VSI and control the switching states of the inverter

Summary

INTRODUCTION

T HE high penetration of generators, loads and storage systems to the main grid, turns power converters and control architectures as main components for a more reliable operation. From the review of existing model-free predictive control strategies we note the need for a systematic approach to construct the prediction model without previous knowledge of the physical system This implies uncertainty in the parameter values, and on the structure of a mathematical model. 2) The proposed approach does not require a detailed knowledge of the physical system, and inherently adapts the prediction model using measured data of the process This makes the controller exceptionally robust to load changes or parameter mismatch. PROPOSED MODEL-FREE PREDICTIVE CONTROL STRUCTURE As discussed, the objective of a predictive current control strategy is to minimize the error between a reference current and its measured values, which is implemented using a cost function such as (5). The main difference of the proposed controller with respect to the conventional FCS-MPC is the approach employed to obtain current predictions. The basic cost function for current reference tracking is applied here for comparing the performances of the proposed method and the conventional FCS-MPC

SYSTEM REPRESENTATION USING AN AUTOREGRESSIVE STRUCTURE

RLS PARAMETER ESTIMATION ALGORITHM

SIMULATION AND EXPERIMENTAL RESULTS

SCENARIO 1

SCENARIO 2

SCENARIO 3

AVERAGE SWITCHING FREQUENCY

CONCLUSION