  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.
