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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2304.11880 |
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| _version_ | 1866911974854492160 |
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| author | Li, Yuyan |
| author_facet | Li, Yuyan |
| contents | Transformer fault diagnosis (TFD) is a critical aspect of power system maintenance and management. This review paper provides a comprehensive overview of the current state of the art in TFD using artificial intelligence (AI) and dissolved gas analysis (DGA). The paper presents an analysis of recent advancements in this field, including the use of deep learning algorithms and advanced data analytics techniques, and their potential impact on TFD and the power industry as a whole. The review also highlights the benefits and limitations of different approaches to transformer fault diagnosis, including rule-based systems, expert systems, neural networks, and machine learning algorithms. Overall, this review aims to provide valuable insights into the importance of TFD and the role of AI in ensuring the reliable operation of power systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_11880 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature Li, Yuyan Systems and Control Machine Learning Transformer fault diagnosis (TFD) is a critical aspect of power system maintenance and management. This review paper provides a comprehensive overview of the current state of the art in TFD using artificial intelligence (AI) and dissolved gas analysis (DGA). The paper presents an analysis of recent advancements in this field, including the use of deep learning algorithms and advanced data analytics techniques, and their potential impact on TFD and the power industry as a whole. The review also highlights the benefits and limitations of different approaches to transformer fault diagnosis, including rule-based systems, expert systems, neural networks, and machine learning algorithms. Overall, this review aims to provide valuable insights into the importance of TFD and the role of AI in ensuring the reliable operation of power systems. |
| title | The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2304.11880 |