Real-Time Implementation of Long-Horizon Direct Model Predictive Control on an Embedded System

This paper deals with the real-time implementation of a long-horizon finite control set model predictive control (FCS-MPC) algorithm on an embedded system. The targeted application is a medium-voltage drive system which means that operation at a very low switching frequency is needed so that the switching power losses are kept relatively low. However, a small sampling interval is required to achieve a fine granularity of switching, and thus ensure superior system performance. This renders the real-time implementation of the controller challenging. To facilitate this, a high level synthesis (HLS) tool, which synthesizes C++ code into VHDL, is employed to enable a higher level of abstraction and faster prototype development of the real-time solver of the long-horizon FCS-MPC problem, namely the sphere decoder. Experimental results based on a small-scale prototype, consisting of a three-level neutral point clamped (NPC) inverter and an induction machine, confirm that the algorithm can be executed in real time within the targeted control period of 25 $\mu$s.publishedVersionPeer reviewe

Direct Model Predictive Control with Constrained Power Losses for Grid-Tied Converters with LCL Filters

This paper proposes a direct model predictive control (MPC) method that improves the efficiency of grid-tied converters with LCL filters, while minimizing the grid current distortions. To do so, the MPC problem not only accounts for the grid current reference tracking minimization, but it also limits the converter power losses by introducing explicit constraints on them. To this aim, different problem formulations are examined to limit the losses. Specifically, the FCS-MPC problem is designed such that it either limits the losses per semiconductor device or the three-phase converter losses. The presented results verify the effectiveness of the proposed strategy and demonstrate the performance benefits compared to conventional control and modulation methods, such as voltage-oriented control (VOC) with space vector modulation (SVM) or discontinuous pulse width modulation (DPWM).Peer reviewe

Optimized Pulse Patterns with Bounded Semiconductor Losses

This paper proposes the computation of three-level optimized pulse patterns (OPPs) that achieve not only low harmonic load current distortions (load-friendly operation) but also low semiconductor losses (converter-friendly operation). To this end, the conduction and switching losses are modeled as a function of the OPP switching angles and the amplitude and phase of the converter current. By minimizing the current harmonics subject to an inequality constraint on the semiconductor losses, OPPs are derived that achieve minimal current distortions with a guaranteed upper bound on the semiconductor losses, thus ensuring the safe operation of the semiconductor switches within their thermal limits. Detailed numerical results for a mediumvoltage system consisting of a neutral-point-clamped converter and an inductive load verify the benefits of this approach.Peer reviewe

Long-Horizon Robust Direct Model Predictive Control for Medium-Voltage Drives with Active Neutral-Point Potential Balancing

The paper presents a direct model predictive control algorithm for medium-voltage (MV) induction machines driven by three-level neutral-point-clamped (NPC) inverters that incorporates the neutral point potential balancing. For such nonlinear systems implementation of long horizons may be regarded as even a formidable task due to the high computational complexity. Nevertheless, this can be achieved with a modest calculation cost by decreasing the size of the underlying control optimization problem. Moreover, when assisted by a light estimation algorithm, the developed control scheme achieves a high level of robustness to variations in the motor parameters. The presented results demonstrate the effectiveness of the proposed method during steady-state and transient operating conditions.Peer reviewe

Loss-Constrained Optimized Pulse Patterns for Three-Level Converters with Robustness to Power Factor Variations

This paper presents the computation of three-level optimized pulse patterns (OPPs) that limit the converter losses and are robust to power factor variations. By constraining the switching and conduction losses in the optimization process the trade-off between converter losses and current harmonic distortions can be improved. Moreover, to increase the solution space of the loss-constrained OPP problem, and thus have more degrees of freedom when establishing the Pareto optimal solutions, the symmetry properties of conventional OPPs are relaxed. Additionally, by constraining the losses not only for a specific power factor but for a range of them, the variation of the losses is small when varying the power factor. As a result, the converter efficiency is improved over a wide range of operating points, as shown with the presented results.Peer reviewe

Implementation of a Long-Horizon Model Predictive Control Algorithm on an Embedded System

This paper deals with the implementation of a long-horizon finite control set model predictive control (FCS-MPC) algorithm on an embedded system. The targeted application is a medium voltage drive system implying a very low switching frequency. The implementation is facilitated by the use of a high level synthesis (HLS) tool, which synthesizes C++ code into VHDL, enabling a higher level of abstraction and faster prototype development. Experimental results based on a small-scale prototype, consisting of a three-level neutral point clamped (NPC) inverter and an induction machine, confirm that the algorithm can be executed in real time within the targeted control period of 25μs. This allows for high switching granularity, and thus favorable steady-state and transient performance.acceptedVersionPeer reviewe

Three-Level Optimized Pulse Patterns With Reduced Common-Mode Voltage

This paper proposes the computation of three-level optimized pulse patterns (OPPs) with reduced common-mode voltage (CMV) over the whole range of modulation indices. Limiting the CMV, however, gives rise to increased current harmonics. To mitigate this, the OPP optimization problem is reformulated to allow for symmetry relaxations and multipolar switch positions. In doing so, the current harmonics not only remain low, but they are occasionally even lower than those of traditional OPPs. The presented numerical results, based on a medium-voltage (MV) drive consisting of a three-level converter and an induction machine, demonstrate the benefits of the proposed approach.acceptedVersionPeer reviewe

A Direct Model Predictive Control Strategy of Back-to-Back Modular Multilevel Converters Using Arm Energy Estimation

This paper presents a model predictive control (MPC) algorithm for modular multilevel converters (MMCs). To meet the control objectives of phase current reference tracking and circulating current minimization, the proposed control scheme calculates the optimal number of submodules (SMs) to be inserted in each arm. In doing so, favorable steady-state and dynamic performance is achieved. More-over, by estimating-instead of measuring-the arm energies in the predictive stage of the control loop, the proposed control scheme results in self-stabilizing open-loop arm energy balancing, while avoiding potential stability issues. Furthermore, to reduce the computational complexity of the MPC algorithm, the optimization problem is simplified by controlling each phase separately and assuming that the SM capacitors are balanced within an arm. To ensure that this assumption is always satisfied, a subsequent capacitor voltage balancing algorithm is designed to select the individual SMs that are switched on and off. The performance of the proposed control strategy is validated with simulations for a high voltage dc system (HVDC) that consists of two MMCs with 20 SMs per arm in a back-to-back configuration.acceptedVersionPeer reviewe

A Computationally Efficient Robust Direct Model Predictive Control for Medium Voltage Induction Motor Drives

Long-horizon direct model predictive control (MPC) has pronounced computational complexity and is susceptible to parameter mismatches. To address these issues, this paper proposes a solution that enhances the robustness of long-horizon direct MPC, while keeping its computational complexity at bay. The former is achieved by means of a suitable prediction model of the drive system that enables the effective estimation of the total leakage inductance of the machine. For the latter, the objective function of the MPC problem is formulated such that, even though the drive behavior is computed over a long prediction interval, only a few changes in the candidate switch positions are considered. The effectiveness of the proposed approach is demonstrated with a medium-voltage (MV) drive consisting of a three-level neutral point clamped (NPC) inverter and an induction machine (IM).acceptedVersionPeer reviewe