Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network

Abstract

A Petri recurrent wavelet fuzzy neural network (PetriRWFNN) controller and a simple presynchronization estimation are proposed for the operations of seamless switching and grid reconnection in a microgrid system. The microgrid using master/slave control consists of a storage system, photovoltaic (PV) system and loads, and can be operated in either grid-connected mode or islanded mode. Since the different control algorithm is adopted in the master distributed generator at different operation modes, the transient deterioration in voltage and active power output of the microgrid system is obvious during the mode switching. Moreover, when the microgrid is operated in islanded mode and the power grid returns to normal operation, the microgrid cannot directly reconnect with the power grid to avoid a large inrush of current and failed grid reconnection due to the asynchronous angle between the islanded microgrid and the grid angle. Therefore, a novel PetriRWFNN controller is proposed to improve the transient responses of the voltage and active power of the microgrid during mode switching. The network structure and the online learning algorithm of the proposed PetriRWFNN are introduced in detail. Furthermore, a simple and fast presynchronization estimation for grid reconnection during the switching from the islanded mode to the grid-connected mode is also proposed in this article. Finally, some experimental results are provided to certify the effectiveness and feasibility of the microgrid system using the proposed PetriRWFNN controller and presynchronization estimation for the operations of seamless switching and grid reconnection.