A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification

The detection and classification of power quality (PQ) disturbances remains a significant challenge because of the rapid integration of renewable energy sources (RES), widespread use of power electronics, and increasing prevalence of sensitive microcontrollers. These evolving PQ issues necessitate the development of accurate and reliable methods for identifying and classifying PQ disturbances. In this paper, we propose a novel model based on a deep convolutional neural network (DCNN) for the feature extraction and classification of PQ disturbances. The architecture of the model was inspired by the visual geometry group (VGG), which is known for its effectiveness in image processing. The extracted features are highly suitable for both multi−class (MC) and multi−label (ML) classification tasks, effectively addressing the complexity of PQ disturbance signals. The ML approach proved its excellence in the classification of complex PQ disturbances. The performance of the model was rigorously evaluated using various metrics across different scenarios, which demonstrated exceptional accuracy and robustness. The model was trained, validated, and tested using synthetically generated data under different signal−to−noise ratio (SNR) scenarios ensuring its effectiveness in practical applications.