This paper presents a detailed description of Finite Control Set Model Predictive Control applied to power converters. Some key features related to this methodology are presented and compared with model predictive control based space vector modulation methods. The basic models, principles, control diagrams, and simulation results are presented to provide a comparison between them. The analysis is performed on a three-phase/ two-level voltage source inverter, which is one of the most common converter topologies used in industry. Among the conclusions are the feasibility and great potential of Finite Control Set Model Predictive Control due to the advanced signalprocessing capability, particularly for power systems with a reduced number of switching states and more complicated principles.
Advances in power electronics and digital control open a new horizon in the control of power converters. Particularly, model predictive control has been developed for control applications in industrial electronics and power systems. This study presents a comprehensive study on recent achievements of model predictive control algorithms to overcome the challenges in the real-time implementation of power converter control, which is the lowest level control of hierarchical control in microgrids. The study shows that most of these alternate solutions can enhance system reliability, stability, and efficiency. The control platform devices for the real-time implementation of these algorithms are compared. The related issues are discussed and classified, respectively. Finally, a summary is provided, leading to some further research questions and future work.
Abstract--This paper aims to find the optimal place and size of an energy storage system in a microgrid, considering the gridconnected mode and autonomous mode simultaneously. Energy storage systems are one of the most effective components in today's power grids to improve the power quality of power grids, therefore attracting more attention in this field. Specially, in microgirds which use various kinds of distributed generations, using energy storage systems is necessary to improve their power quality. Finding the optimal place and size of energy storage systems is a common action in microgrids. However, it should be noted that most microgrids can be operated in both of their operation modes and finding optimal place and size of an energy storage system for one of these operation modes doesn't mean that they are optimal for the other mode. This paper presents a new method to find the optimal place and size of an energy storage system for microgrids during daily operation, considering both grid-connected mode and autonomous mode simultaneously. The presented method is based on applying the AC-optimal power flow to find the optimal place and size of the energy storage system.
This paper proposes a self-tuning model predictive direct power control (MPDPC) strategy for power flow control and power quality improvement in gridconnected power converters. At each sampling instant, a fuzzy logic controller is used to determine online the best weighting factor values for a given operating point. These values are then used to solve the multi-objective optimal control problem associated to the MPDPC. The optimal solution that minimizes the multi-objective cost function is chosen as the input (power switch state). The proposed method is examined through a case study and verified numerically via MATLAB SIMULINK. A comparative study is conducted to demonstrate the effective performance of this approach. As a result of the proposed weighting factor online tuning, an improved performance in terms of total harmonic distortion and average switching frequency is attained when compared with fixed weighting factors.
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