Islanding detection using bi-directional energy meter in a DFIG based active distribution network

With the deregulation of the electrical power market, the distribution architecture has now heavily integrated distributed generation (DG) technologies. The presence of DGs has reformed power grid management leading to concepts such as micro-grid technology. Despite its huge advantages like smoothing power grid operations, improving power quality and managing capricious demand, DGs have limitations like unplanned islanding. Prolonged accidental islanding can seriously damage utility resources and compromise the safety and serviceability. Thus, it's critical to have a protection scheme which enables quick and reliable islanding detection. Though many techniques have been developed recently, they lack either consistency or swiftness. The proposed methodology is based on voltage measurement directly from a bi-directional energy meter and processing of these signals with constant-Q transform (CQT). This technique determines, if the abnormity in a micro-grid is because of any fault or an island operation by using artificial neural network (ANN). The proposed algorithm and its efficiency is demonstrated and justified with MATLAB simulations. Various islanding events as well as non-islanding events in a wind based DG micro-grid is considered for the proposed method.