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.

最近，人们对利用机器学习进行电能质量事件（PQE）的准确分类越来越感兴趣。本文在先前的 PQE 分类研究基础上，提出了一个深度学习框架，利用注意力机制和 Transformer 来准确地在考虑到测量噪音、直流偏移以及电压信号幅值和频率变化的情况下进行 PQE 分类。实验证明，该框架的性能优于最近提出的基于学习的技术，其分类准确率在信噪比、直流偏移和信号幅值、频率变化等条件下能达到 99.81% 到 91.43% 不等。