Customized CNN based classification of power system disturbances using recurrence plots

This work proposes a Recurrence Plot (RP) based customized convolution neural network (CNN) method for the power system disturbance classification of (Distributed Generation) DG-based networks. All feasible disturbance-creating events including faults, different switching events and dual disturbances have been considered here. The proposed method has been applied to the disturbance signals generated from a laboratory- based experimental setup of DG. Those signals were converted to RP images to train the CNN algorithm. In addition, recurrence quantification analysis (RQA) was performed to train the Support Vector Machine (SVM) simultaneously for comparison. Performance indices have been estimated to validate the proposed method. This algorithm has also been tested under highly noisy conditions and performs satisfactorily.