Droop-Controlled Microgrid Using Intelligent Presynchronization for Grid Connection

Abstract

In this study, a novel presynchronization method is proposed for a droop-controlled microgrid to achieve grid connection. When the microgrid operates in islanded mode and intends to reconnect to the power grid, a seamless transition between islanded mode and grid-connected mode is challenging because of phase, frequency, and voltage mismatches. If the microgrid is not synchronized with the power grid, inrush currents and voltage fluctuations may lead to system instability and damage equipment during grid connection. Hence, a novel presynchronization method is proposed to achieve phase synchronization, frequency, and voltage restoration for the droop-controlled microgrid. Moreover, two binarized probabilistic Legendre fuzzy neural network (BPLFNN) controllers are proposed to replace the traditional proportional–integral (PI), PI-derivative neural network (PIDNN), and fuzzy neural network (FNN) controllers for frequency and voltage restoration. Finally, the effectiveness of the proposed BPLFNN-based presynchronization method to rapidly achieve grid connection is verified by experimentation. The voltage and frequency restoration times are significantly reduced to 0.72 and 0.66 s, which are superior to the PI controllers (1.14 and 1.55 s), PIDNN controllers (1.06 and 1.18 s), and FNN controllers (0.91 and 1.04 s).