# Enhancing Power Quality Event Classification with AI Transformer Models

arXiv: https://arxiv.org/abs/2402.14949 · HTML: https://mlsanity.com/paper/2402.14949/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [Improving Generalization Performance by Switching from Adam to SGD](https://mlsanity.com/paper/1712.07628/) (2017)
- [Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters](https://mlsanity.com/paper/2309.10304/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Novel Approach to Classify Power Quality Signals Using Vision Transformers](https://mlsanity.com/paper/2409.00025/) (2024)

## Abstract (arXiv)

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%-91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.