Finite Control Set Model Predictive DC-Grid Voltage Estimation Control in DC-Microgrids

Using a linear Kalman filtering approach (LKFA), the finite-control-set model predictive control (FCS- MPC) proposes for DC-grid voltage estimation control in direct current microgrid systems (DCMS). The proposed control algorithm technique addresses real-time measurements needed to implement the FCS-MPC by adopting the LKFA for real-time application. The state-space model is used to produce the second DCMS predictive model, which provides access to a sample time for the performance of an efficient algorithm. The dynamic model of DCMS is transformed into a stationary linear stochastic discrete time-invariant system to standardize the state estimation design. The DC-grid voltage estimation reference is computed leveraging LKFA and a state feedback control law based on the dynamic algebraic Riccati equation with integral action. The proposed algorithm has been verified under varying load demands. Fast computations, DC-grid voltage estimation, and equally estimated power-sharing between DERs have all been realized.