Weighting Factors Design of CCS-MPC for a Grid-Connected DFIG Systems

The model predictive control (MPC) is commonly used in power systems, especially in renewable energy generation, due to its fixed switching frequency and multivariable control. In this context, application to the doubly fed induction generator (DFIG) minimizes torque and current oscillations, leading to greater stability in dynamics. A significant challenge in the design of the weighting factors within the MPC is that inappropriate selections can adversely affect the system’s performance, given their direct impact on the cost function. The methodology presented aims to simplify the design of the weighting factors in the MPC applied to the DFIG using the root locus method (RLM), based on ensuring the stability of closed-loop eigenvalues while optimizing performance related to power regulation and energy quality. Furthermore, performance maps were created to evaluate the effects of weighting factors and predictive horizons on system dynamics. Therefore, this article proposes to design MPC’s weighting factors to ensure stability and performance of grid-injected energy by regulating DFIG rotor currents, with experimental validation on a test bench.