IJCAS

This paper proposes a data-driven approach strategy for enhancing the performance of grid forming converters (GFCs) in microgrids by leveraging the capabilities of dynamic mode decomposition (DMD) in combination with finite-control-set model predictive control (FCS-MPC). Conventional FCS-MPC, based on static models, have encountered numerous challenges in addressing parametric uncertainties in microgrid applications. To address this, the proposed strategy introduces an adaptive model based on DMD, integrated into the FCS-MPC framework to yield a more robust and reliable control technique in the presence of parametric uncertainties. The proposed datadriven approach utilizes the DMD-based model in combination with FCS-MPC to effectively share power through primary control and maintain voltage and frequency stability through secondary control, thus achieving improved reference tracking, load power variation robustness, and power quality. The effectiveness and efficiency of this proposed data-driven approach were validated through a comparative study with conventional static model FCS-MPC and double loop PI control, utilizing the MATLAB/Simulink platform.

Keywords: Data-driven approach, dynamic mode decomposition (DMD), finite-control-set model predictive control (FCS-MPC), grid forming converter (GFC), microgrid, primary and secondary control.