EKF-Based Predictive Stabilization of Shipboard DC Microgrids With Uncertain Time-Varying Load

Abstract

The performance of dc shipboard power systems (SPSs) may degrade due to the negative impedance of constant power loads (CPLs) connected to dc microgrids (MGs). To control the dcSPS effectively, estimation of the instantaneous power flow to the time-varying uncertain CPLs is necessary. Furthermore, fast adaptive control is needed to deal with changes in the CPL power flow and quick stabilization of the dc MGs. Such a controller typically uses injection current from an energy storage system for actuation. Since measuring the CPLs’ powers require installing current sensors that are both costly and not optimal, an estimation of the CPLs’ powers should be employed. In this paper, an extended Kalman filter (EKF) is developed to estimate a time-varying power of uncertain CPLs in a dc MG based on measuring capacitor voltages. The estimated power is then used in a Takagi–Sugeno fuzzy-based model predictive controller (MPC) to manipulate the energy storage unit. The proposed approach is tested experimentally on a dc MG that feeds a single CPL. The experimental results show that the proposed MPC controller alongside the developed EKF improves the transient performance and the stability margin of the dc MGs used in the SPSs.