Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front-end converters for distributed energy resources

The operational performance of grid-connected active front-end converters (AFEs) faces challenges arising from the intricate interplay among phase-locked loop (PLL) non-linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS-based MPC approach, equipped with an auto-tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non-linearities. The method adeptly integrates inherent non-linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real-time emulators, providing tangible evidence of the proposed control system's effectiveness in real-world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto-tuning, disturbance rejection, robust design, and real-time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.