Published Paper Details:

The protection of microgrids (MGs) is one of the most important and dangerous operational challenges with the gradual implementation of renewable energy sources in recent power systems. MGs are generally combined with photovoltaic (PV) arrays, wind turbines, fuel cells. Fault detection in MG is very complicated due to complex structures and so many bus bars available in MG, so fault detection and classification is necessary for MG operation and control, as it allows the system to perform fast fault isolation and recovery. Otherwise, MG component like transformer, loads, generators, and insulator may get damage due to long-duration faults presents in the system. In this paper, an intelligent fault detection method for MG based on wavelet transform (WT) and neural network (NN) is used. The main objective of the proposed scheme is to provide fast fault type information for MG protection and recovery. In this scheme, branch currents are pre-processed by discrete WT to extract statistical features. Then all the available data is given as input to NNs to developed fault information. All the tests are conducted on the IEEE 14 bus system.