Modeling and Fuzzy Predictive Voltage Control of VSC-Based Microgrids

Microgrids have become one of the most challenging areas of power systems in recent years. Microgrid control methods often require a complete model of the system. In this paper, state-space model of DGs, network, and loads as components of microgrids is studied. Nominal set points of the output voltage are used as input for the secondary voltage control. PI controller is the traditional method of controlling frequency and voltage in microgrids. Model predictive control (MPC) is another effective method for this purpose, in which future outputs are predicted based on the current state of the system, and the optimized input signal is generated accordingly. The closed loop system response, achieved by employing parameter-driven MPC, is greatly dependent on the controller parameters. Therefore one can reduce voltage deviation in the system by proper adjustment of MPC parameters. In this paper, traditional fixed-parameter MPC has been made adaptable by dynamically adjusting its parameter
$R_{w}$
using a Mamdani type fuzzy logic controller (FLC). Closed loop performance obtained by the proposed fuzzy MPC has been analyzed on a microgrid with three DGs and three loads. Effectiveness has been evaluated in comparison with fixed-parameter MPC response. This then validates the efficiency of the proposed fuzzy MPC secondary voltage control.