A Novel Approach to Classify Power Quality Signals Using Vision Transformers
This addresses the need for accurate classification of power quality disturbances in smart grids, but it is incremental as it applies an existing method to a new domain with improved performance.
The paper tackles power quality disturbance classification in smart grids by converting signals to images and using a Vision Transformer, achieving 98.28% precision and 97.98% recall on a dataset with 17 classes.
With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.