SMART GRID ISLANDING, FAULT DETECTION AND CLASSIFICATION WITH DISTRIBUTED GENERATION BASED ON WAVELET ALIENATION CURRENT SIGNALS APPROACH

The Distributed generation (DG) is gaining significant attention due to increase in the demand for electricity. Distributed generations are mostly used with the association of power distribution systems to energize local loads and network. Islanding is technically an undesirable condition that demands necessary steps to reduce negative effects in maintaining stability of the system. In the present work, alienation technique has been applied to detect and differentiate islanding, faults and sudden load change and faults have been classified. A radial system with four Distributed Generations (DFIG wind generator) connected to the source through common coupling point (PCC) has been used for comprehensive study of this technique. Current signals were decomposed with Daubechies (db1) wavelet in order to get approximate coefficients at each bus. Coefficients of Alienation are computed by using the wavelet based approximations over a length of half cycle moving window . The alienation coefficients were used to compute Islanding index and fault index. The same indices were compared with threshold to differentiate Islanding, faults and sudden load change. The proposed algorithm has been tested for various incidence angles for both islanding condition and faults. This technique is established to be robust in detecting islanding condition, faults and impact of sudden load change.