A Novel Approach to Classify Power Quality Signals Using Vision Transformers

TL;DR

This paper presents a new method for classifying power quality disturbances by converting signals into images and using Vision Transformers, achieving high accuracy on a large dataset with 17 classes.

Contribution

The paper introduces a novel ViT-based approach for PQD classification that outperforms existing methods on a large, multi-class dataset.

Findings

Achieved 98.28% precision and 97.98% recall in PQD classification.

Outperformed recent techniques on the same large dataset.

Demonstrated effectiveness of Vision Transformers in power quality analysis.

Abstract

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.