Energy storage systems have been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper proposes a modified model predictive control (MMPC) method based on the Lyapunov function to improve the performance of a bidirectional ac-dc converter, which is used in an energy storage system for bidirectional power transfer between the three-phase ac voltage supply and energy storage devices. The proposed control technique utilizes the discrete behavior of the converter, considering the unavoidable quantization errors between the controller and the control actions selected from the finite control set of the bidirectional ac-dc converter. The proposed control method reduces the execution time delay by 18% compared with the conventional model predictive control. Moreover, the nonlinear system stability of the proposed MMPC technique is ensured by the direct Lyapunov method and a nonlinear experimental system model. Detailed experimental results with a 2.5-kW downscaled hardware prototype are provided to show the efficacy of the proposed control system.

Tuning of weighting factors is a very complex task in the implementation of conventional finite set predictive torque control (FS-PTC) for 3L-NPC inverter fed IM drive. The choice of these weighting factors strongly influences the control objectives such as flux, torque and neutral point voltage (NPV). Moreover, the computational burden is high as all 27 switching states are enumerated for the evaluation of a cost function involving multiple objectives. To reduce the complexity of the conventional FS-PTC, this paper proposes a high-efficient single-vector-based predictive flux control (PFC) for an IM drive fed by a three-level NPC inverter. The proposed cascaded predictive flux and NPV control evaluates 13 vectors for outer predictive flux control and 2 switching states for the inner predictive NPV control. It significantly reduces the computational load as the number of enumerations is reduced by more than 50%. The nontrivial process of weighting factor calculation is no longer required. The complexity reduction and the elimination of weighting factors are achieved without sacrificing the transient and steady-state performances in terms of torque and stator flux responses, total harmonic distortion of stator currents, NPV variation and average switching frequency, compared to the conventional method as verified by experimental results.

This article presents an alternative implementation of finite control set model predictive control (FCS-MPC) approach for a 3-phase, two-level grid-connected power converter. The presented approach combines the concept of discrete space vector modulation (DSVM) into the FCS-MPC structure to improve the input current quality of the power converter. Further, with the help of Lyapunov's stability theory, it is shown that the predictive control law used in the present analysis ensures the stability of the overall system. However, the additional virtual voltage vectors can cause an unacceptable computational burden to the controller. To alleviate this issue, the proposed control approach uses a preselection technique which reduces the number of possible voltage vectors for the optimization process from thirty-eight to ten, thereby allowing the proposed technique to be implemented in a standard control platform. Finally, measurement results are presented from an experimental prototype which demonstrates the efficacy of the proposed algorithm concerning the improvement of the input current quality.