Machine Learning-Enhanced Predictive Control for Modular Multilevel Converters in HVDC Transmission Systems

Modular Multilevel Converters (MMCs) are pivotal components in High Voltage Direct Current (HVDC) transmission systems, underpinning the advancement of contemporary power grids. Their operational efficiency and reliability are of paramount importance for the stability of interconnected networks. Traditional control methodologies, although robust, often fall short in swiftly adapting to dynamic grid conditions. This research elucidates the integration of machine learning (ML) techniques to enhance the predictive control of MMCs in HVDC systems. By leveraging historical and real-time data, the proposed ML-based predictive control algorithm optimizes switching patterns, reduces commutation losses, and ensures voltage quality. Simulation results, compared against conventional control methods, manifest marked improvements in efficiency, reduced transient response times, and enhanced fault tolerance. Furthermore, the incorporation of ML provides predictive insights into potential system anomalies, allowing for preemptive actions to enhance system robustness. The outcomes of this study pave the way for the integration of AI-driven methodologies in HVDC systems, revolutionizing the field of power electronics and transmission systems.