Wide area monitoring and protection of microgrid with DGs using modular artificial neural networks

The prominence of incorporating the renewable energy resources in a power system via microgrids has increased in the recent years, which impose a caution on conventional protection schemes. Protection schemes proposed earlier use local measurements, but fault classification for selective phase tripping using wide area measurements for microgrid has not been reported so far. This paper presents a wide area monitoring and protection of microgrid with distributed generations (DGs) using modular artificial neural networks (MANNs) for the fault detection and classification without affecting the relays in non-faulty or healthy sections of the microgrid. The distinct characteristics of the microgrid sort the proposed methodology into two stages. In stage 1, ANN 1 is developed to identify the operating mode of microgrid, whether it is operating in grid-connected mode (GCM) or islanded mode (IM). In stage 2, there are two MANNs corresponding to GCM and IM. Each MANNs consists of three separate ANNs for fault detection, classification, and section identification. A standard IEC 61850-7-420 microgrid with DGs (wind and photovoltaic) penetration is modeled in MATLAB/Simulink. The three-phase voltages and currents are measured with time synchronization considering the microphasor measurement units located at each bus. The extensive study includes different simulation scenarios such as shunt faults, high impedance fault, and dynamic situations like connection/disconnection of DGs/distribution lines. The results confirm the efficacy of the proposed methodology.