Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks

This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a WVD technique is developed to transfer a 1D voltage disturbance signal into a 2D image file, followed by a CNN model developed for the image classification. Then, the feature maps are extracted automatically from the image file and different patterns are extracted from variables on CNN. A set of synthetic signals, as well as real-world measurement data, are used to test the proposed method. The high classification accuracy of test results is achieved to confirm the effectiveness of the proposed method. Furthermore, the model is simplified and optimized by visualizing the output of convolutional layers. On this basis, one visualizing technique called the class activation map (CAM) is used to identify the location and shape of “hotspots (PQDs)”. The effect of incorrect classification of the model is analyzed with the CAM. Therefore, the proposed method is proved to have the capability of providing necessary and accurate information for PQDs, which will then be used to determine the subsequent PQ remedy actions accordingly.