Imaging Time-Series Technique with CNN for Power Quality Disturbances Classification

This work presents a framework to accomplish the power quality disturbances classification by employing a novel hybrid methodology that combines the Gramian Angular Difference Field (GADF) approach with a deep neural network. The Gramian Angular Difference Field (GADF) method is applied to convert Power quality signals to two dimensional image and then, convolutional neural network (CNN) is applied to learn high level features for classification purpose. The mixed and single synthetic power quality (PQ) disturbances are taken into account for classification purpose. A unit is constructed with two dimensional convolutional, pooling, and batch-normalization layers to extract the meaningful features of the power quality disturbances. The GADF-based convolutional neural network (GADF-CNN) for classifying PQ disturbance signals is validated subsequently. The results shows that the proposed classification model has high efficiency and reliability.