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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Accesso online: | https://arxiv.org/abs/2305.19390 |
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| _version_ | 1866917655189913600 |
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| author | Sami, Naeem Md Naeini, Mia |
| author_facet | Sami, Naeem Md Naeini, Mia |
| contents | Cascading failures pose a significant threat to power grids and have garnered considerable research interest in the power system domain. The inherent uncertainty and severe impact associated with cascading failures have raised concerns, prompting the development of various techniques to study these complex phenomena. In recent years, advancements in monitoring technologies and the availability of large volumes of data from power systems, coupled with the emergence of intelligent algorithms, have made machine learning (ML) techniques increasingly attractive for addressing cascading failure problems. This survey provides a comprehensive overview of ML-based techniques for analyzing cascading failures in power systems. The survey categorizes these techniques based on the evolutionary phases of the cascade process in power systems, as well as studies focusing on cascade resiliency before the occurrence of cascades and problems related to cascades after their termination. By organizing and presenting these works into relevant categories, this survey aims to offer insights and a systematic understanding the role of ML in mitigating cascading failures in power systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_19390 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine Learning Applications in Cascading Failure Analysis in Power Systems: A Review Sami, Naeem Md Naeini, Mia Systems and Control Cascading failures pose a significant threat to power grids and have garnered considerable research interest in the power system domain. The inherent uncertainty and severe impact associated with cascading failures have raised concerns, prompting the development of various techniques to study these complex phenomena. In recent years, advancements in monitoring technologies and the availability of large volumes of data from power systems, coupled with the emergence of intelligent algorithms, have made machine learning (ML) techniques increasingly attractive for addressing cascading failure problems. This survey provides a comprehensive overview of ML-based techniques for analyzing cascading failures in power systems. The survey categorizes these techniques based on the evolutionary phases of the cascade process in power systems, as well as studies focusing on cascade resiliency before the occurrence of cascades and problems related to cascades after their termination. By organizing and presenting these works into relevant categories, this survey aims to offer insights and a systematic understanding the role of ML in mitigating cascading failures in power systems. |
| title | Machine Learning Applications in Cascading Failure Analysis in Power Systems: A Review |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2305.19390 |