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A simplified model predictive control for shunt active power filter using modified instantaneous power theory under polluted grid conditions

Bouchikha Hocine, Mesbahi Nadhir, Heguig Larouci, Ouari Ahmed

Journal of Power Technologies

2022

Vol. 102, nr 1

14--24

This paper proposes a simplified finite control set model predictive control (FCS-MPC) strategy for a three-phase shunt active power filter (SAPF), which is based on a vector operation technique (VOT). In the conventional FCS-MPC, the optimal switching state is selected based on the evaluation and minimization of a cost function for all possible voltage vectors of the voltage source inverter (eight different vectors). The proposed FCS-MPC performs like a conventional FCS-MPC where the selection and evaluation of the possible voltage vectors are reduced by half (four vectors). The reduction in the computational burden is evident. In this study, the modified version of the instantaneous power theory based on a high selectivity filter is used to extract reference current components which increase the selectivity and the dynamic performance of SAPF. Simulation results demonstrate the effectiveness and reliability of the SAPF with the proposed control strategy under polluted grid conditions.

Cascade tanks system identification for robust predictive control

Jakowluk Wiktor, Jaszczak Sławomir

Bulletin of the Polish Academy of Sciences. Technical Sciences

2022

Vol. 70, nr 6

art. no. e143646

The main goal of estimating models for industrial applications is to guarantee the cheapest system identification. The requirements for the identification experiment should not be allowed to affect product quality under normal operating conditions. This paper deals with ensuring the required liquid levels of the cascade system tanks using the model predictive control (MPC) method. The MPC strategy was extended with the Kalman filter (KF) to predict the system’s succeeding states subject to a reference trajectory in the presence of both process and measurement noise covariances. The main contribution is to use the application-oriented input design to update the parameters of the model during system degradation. This framework delivers the least-costly identification experiment and guarantees high performance of the system with the updated model. The methods presented are evaluated both in the experiments on a real process and in the computer simulations. The results of the robust MPC application for cascade system water levels control are discussed.