Experimental Validation of a Robust Continuous Nonlinear Model Predictive Control Based Grid-Interlinked Photovoltaic Inverter

Abstract

Abstract-The paper presents a robust continuous nonlinear model predictive control (CNMPC) for a grid-connected photovoltaic (PV) inverter system. The objective of the proposed approach is to control the power exchange between the grid and a photovoltaic system, while achieving unity power factor operation. As the continuous nonlinear MPC cannot completely remove the steady-state error in the presence of disturbances, the nonlinear disturbance observer-based control is adopted to estimate the offset caused by parametric uncertainties and external perturbation. The stability of the closed-loop system under both nonlinear predictive control and disturbance observer is ensured by convergence of the output-tracking error to the origin. The proposed control strategy is verified using a complete laboratoryscale PV test-bed system consisting of a photovoltaic emulator, a boost converter, and a grid-tied inverter. High performance with respect to DC-link voltage tracking, grid current control, disturbance rejection, and unity power factor operation has been demonstrated.

A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method

“…Paper [13] has validated that CCS-MPC based methods have better performance and reduced computational burden in power electronic system applications. In this paper, a CCS-MPC based control method for grid-connected photovoltaic (PV) systems.…”

“…In most of existing applications of MPC for power converters, the control command of next period is directly selected from a finite set of switching states according to the cost function, namely finite-control-set MPC (FCS-MPC) [9][10][11][12]. While in some other approaches, the output variables, such as the current or voltage references, are continuous values, and these methods are known as continuous control set MPC (CCS-MPC) [13,14].…”

“…However, FCS-MPC is not suitable in the cases when the optimal switching state cannot be directly selected or there are considerably number of switching states. The three major drawbacks of FCS-MPC are: (i) High computational burden due to evaluation of all the switching states; (ii) low control accuracy due to limited switching states; (iii) model dependency [13].…”

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

“…Yaramasu et al discussed about the Model predictive control of wind energy conversion systems [23]. Experimental validation of a robust continuous nonlinear model predictive control based gridinterlinked photovoltaic inverter was reported by Errouissi et al [24]. Cheng et al designed model predictive control for DC-DC boost converters with reduced-prediction horizon and constant switching frequency [25].…”

“…Despite many developments in MPC, some problems are still exist. New theoretical methods have proven that MPC is better than many other control strategies with an intermediate computational complexity [24][25][26][27][28][29]. The non-linear dynamics were approximated as a piecewise linear (INTEGRAL CONSTANT) 13.62 model [30], and then the MPC is applied.…”