A deep CNN approach for islanding detection of integrated DG with time series data and scalogram

The ever increasing demand for electricity leads to the advancement of distributed generation (DG). Almost all DG sources are renewable nature. One of the major complications with the high penetration of DG sources is islanding. The islanding may damage the clients and their equipment. As per the IEEE 1547 DG interconnection standards, the islanding will be identified in two seconds and the DG must be turned off. In this paper, an advanced islanding detection process stands on a deep learning technique with continuous wavelet transforms and convolution neural networks implemented. This approach transforms the time series information into scalogram images, and later, the images are used to train and test the islanding and non-islanding events. The outcomes are correlated with the artificial neural networks and fuzzy logic methods. The comparison shows that the proposed deep learning approach efficiently detects the islanding and non-islanding events.