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This article addresses a novel finite control-set model predictive control solution for nested neutral point-clamped converter. More specifically, it is formed by incorporating a finite-time extended state observer design into a cascade-free finite control-set model predictive control architecture. In this architecture, a finite-time extended state observer is developed to estimate the total disturbances, and an integral error term and a dynamic reference design point of view are embedded into the proposed design so as to improve the performance. The main features of this proposal are: first of all, the detailed knowledge of the physical structure of the controlled plant is not required; second, a cascade-free structure is achieved in finite control-set model predictive control solution without weighting factors and steady-state error; and third, the proposed methodology can operate with different systems not just power converters, while keeping the fast transient response. In contrast to existing finite control-set model predictive control approaches, it can be found that our modification not only can track fast to their references but also can have superior robustness performance. Finally, the proposed solution is verified by comprehensive results that prove the theoretical researches for nested neutral point-clamped converter, while meeting the harmonic standards. [ABSTRACT FROM AUTHOR]

To address the requirement of improved power capacity, reliability and operation, parallel converters have been widely applied in micro-grid. However, the connections between DC and AC sides of different parallel converters may lead to the surge of zero-sequence circulating current (ZSCC), which would result in the deterioration of system functionality. Regarding the stated problem above, this paper proposes an improved data-driven based model predictive control for parallel converters. Specifically, a model-free adaptive control (MFAC), which is based on the measured input/output data, is incorporated into the finite control-set model predictive control (FCS-MPC) framework with multi-objective constraints, thus allowing for the suppression of ZSCC and the improvement of robustness performance. Furthermore, to avoid the tuning of weighting factors, a novel cost function is constructed based on the multi-objective decision making method. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties. Furthermore, by this proposal, the accurate current tracking and ZSCC suppression can be achieved. Finally, the validity of the proposed FCS-MPC scheme is verified by comprehensive case studies. • Parallel converters may lead to the surge of zero-sequence circulating current. • An improved data-driven based model predictive control is proposed. • No need for model and parameters, only based on the measured input/output data. • Accurate current tracking and zero-sequence circulating current can be achieved. • Avoidance of the weighting factors tuning and enhanced robustness is realized. [ABSTRACT FROM AUTHOR]

• The main contribution of the proposed solution relies on the fact that no online optimization and weighting factors as well as parameters information in whole control process are required, while guaranteeing adaptability to different conditions. • Compared with the state-of-the art predictive control strategies, the novelty of the proposed solution is that no online optimization and weighting factors tuning as well as parameters information in whole control process are required. • A KF-based SM voltage online estimation technique along with a currentless sorting strategy is developed, resulting in a simpler implementation and a lower associated costs. Alternatively, the stability of the closed-loop system is analyzed. In this paper, we propose a simple and efficient model-free predictive power control for active front-end modular multilevel converter. The main contribution of the proposed solution relies on the fact that no online optimization and weighting factors as well as parameters information in whole control process are required, while guaranteeing adaptability to different conditions. To be specific, it is realized by cascading an efficient model-free predictive power control based on extended state observer and a Kalman filter-based submodule voltage estimation technique, which is responsible for enhancing the robustness and reliability of the control system in the presence of parametric uncertainties. As such, unlike the conventional scheme that needs to evaluate the cost function, the philosophy behind the proposed methodology is to reduce the control complexity by eliminating the online optimization and weighting factors, which leads to a significant reduction in the calculation effort without sacrificing the performance. Alternatively, the stability of the closed-loop system is analyzed. Finally, the proposed methodology is experimentally assessed for active front-end modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal. [ABSTRACT FROM AUTHOR]

• For the first time, a Lyapunov-based finite control-set model predictive control (FCS-MPC) is proposed for nested neutral point-clamped converters (NNPCs) without online weighting factors tuning. • The contribution of the proposed approach is twofold. The first one is the elimination of the weighting factors by combining the proposed methodology with the Lyapunov control theory while guaranteeing adaptability to different conditions. The second one is the integration of a dynamic reference design technique with a computationally efficient FCS-MPC scheme which achieves the satisfactory regulation performance, while maintaining fast dynamic performance and flying capacitor voltages balancing. • The novelty of this paper lies not only in combining the proposed methodology with the Lyapunov control theory for avoiding the tedious tuning work of weighting factors, but also in a compatible reference technique that directly allows formulating the optimal control problem using a cascade-free structure without the intervention of a PWM block. This paper presents a novel finite control-set model predictive control (FCS-MPC) strategy for solving the well-known challenges in predictive control regulated NNPCs, which aims to avoid the tedious online adjustment of weighting factors and to achieve the flexible power regulation. Firstly, a modified cost function, which is inspired by Lyapunov control theory, is formulated from system stability point of view. As such, the designed Lyapunov-based cost function has a good potential to avoid the selection of weighting factors for the conventional FCS-MPC scheme, especially in the presence of multiple control objectives. Next, in order to achieve satisfactory regulation performance, a dynamic reference design technique is introduced as an alternative solution to serve this purpose. Worth to note that the computational complexity will be increased while applying the proposed design methodology to the control of NNPCs. In this sense, a computationally efficient FCS-MPC strategy based on Lyapunov theory is presented here. Therefore, the overall computational burden is significantly decreased while maintaining fast transient performance and flying capacitor voltages balancing. Finally, compared with the state-of-the-art FCS-MPC strategies, the effectiveness and feasibility of the proposed methodology for NNPCs are confirmed through comprehensive simulation and experimental results. [ABSTRACT FROM AUTHOR]