Enhancing Power Quality Event Classification with AI Transformer Models
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910341554765824 |
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| author | Saber, Ahmad Mohammad Youssef, Amr Svetinovic, Davor Zeineldin, Hatem Kundur, Deepa El-Saadany, Ehab |
| author_facet | Saber, Ahmad Mohammad Youssef, Amr Svetinovic, Davor Zeineldin, Hatem Kundur, Deepa El-Saadany, Ehab |
| contents | 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%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Power Quality Event Classification with AI Transformer Models Saber, Ahmad Mohammad Youssef, Amr Svetinovic, Davor Zeineldin, Hatem Kundur, Deepa El-Saadany, Ehab Machine Learning Signal Processing Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency. |
| title | Enhancing Power Quality Event Classification with AI Transformer Models |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2402.14949 |