Neural Network-based Model Predictive Controller for Modular Multilevel Converters
Modular Multilevel Converter , Model Predictive Control , Neural Networks
This thesis explores advanced control strategies for Modular Multilevel Converters (MMCs) to meet the growing demand for efficient and reliable power conversion systems. It compares two approaches: Model Predictive Control (MPC) and Neural Network trained with MPC data. These strategies aim to enhance MMC performance, adaptability, and reduce the computational burden in high-voltage applications. The thesis begins by introducing the fundamental principles of MPC and its application in MMCs. It explores the benefits and limitations of classical control methods, motivating the need for advanced control strategies. The MPC approach is discussed in detail, highlighting its ability to achieve fast dynamic response, eliminate traditional proportional-integral (PI) regulators and pulse width modulation (PWM) schemes, and handle multiple control objectives. Additionally, the integration of neural networks into MMC control is explored. It emphasizes its advantages, such as approximation capabilities and adaptability with reduced computational burden compared to MPC. The training process for the neural network controller, encompassing data collection, network architecture selection, and training methodologies is explained. The thesis presents simulation results and comparative analyses of the MPC and neural network control. The performance of each control strategy is evaluated based on different control objectives, such as output current tracking, voltage balancing, and robustness to parameter uncertainties. The results demonstrate the effectiveness of both approaches in improving the control performance of MMCs. Furthermore, the thesis investigates online weighting factor selection in MPC to improve adaptability. It discusses dynamically adjusting weighting factors based on system conditions and demonstrates the enhanced performance of neural network control with online weighting factor selection through simulation results. In conclusion, this thesis provides valuable insights into advanced control strategies for MMCs. It highlights the benefits of both MPC and neural network control approaches in terms of improved control performance and adaptability. The comparative analysis contributes to a better understanding of the strengths and limitations of each approach, enabling researchers and practitioners to make informed decisions regarding control strategy selection for MMC-based systems. The findings of this thesis pave the way for further advancements in control techniques for MMCs, facilitating the development of more efficient and reliable power conversion systems.