Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances

Abstract

Power quality disturbances (PQDs) are major obstacles to maintaining the reliability and stability of electrical systems. This study introduces a new multi-scale deep learning method to classify PQDs, aiming to enhance the accuracy and efficiency of power quality (PQ) analysis and monitoring systems. By combining 1-D convolutional neural networks (CNNs) with an attention mechanism, this approach overcomes the limitations of traditional techniques. Moreover, varying-size convolutional layers allow for the direct learning of complex patterns and features from PQ signals. To address the challenge of limited labeled PQ datasets, this research utilizes an open-source dataset generator to create large-scale datasets with annotated PQDs. Through a comparison with existing models in the field, the superiority of the proposed CNN-based approach is evident, achieving an accuracy level of up to 99.49%. The results demonstrate promising classification performance in terms of simplicity and accuracy, highlighting the potential of this approach to improve PQ analysis and disturbance identification.

Keywords

• power quality disturbance
• multi-scale CNN
• deep learning
• attention mechanism
• feature extraction