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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.08914 |
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| _version_ | 1866916360887468032 |
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| author | Zhang, Runhao |
| author_facet | Zhang, Runhao |
| contents | This review comprehensively examines the integration of artificial intelligence (AI) in enhancing the dynamic security assessments of modern power systems. It highlights the pivotal role of AI in facilitating scenario generation, incident prediction, risk assessment, and severity grading, thereby addressing the complexities introduced by renewable energy integration and advancements in digital grid technologies. The paper delves into data-driven techniques, with a particular focus on decision trees that effectively bridge operational characteristics with security metrics. These methodologies enable real-time, accurate predictions of system behaviors under varied operational conditions and support the optimization of control strategies. Through detailed analysis, we demonstrate how AI applications can transform traditional security assessment protocols, enhancing both the efficacy and efficiency of power system operations. The findings advocate for the potential of AI to significantly enhance the reliability and resilience of electrical grids, marking a paradigm shift towards more adaptive and intelligent power infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08914 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence in Power System Security and Stability Analysis: A Comprehensive Review Zhang, Runhao Systems and Control This review comprehensively examines the integration of artificial intelligence (AI) in enhancing the dynamic security assessments of modern power systems. It highlights the pivotal role of AI in facilitating scenario generation, incident prediction, risk assessment, and severity grading, thereby addressing the complexities introduced by renewable energy integration and advancements in digital grid technologies. The paper delves into data-driven techniques, with a particular focus on decision trees that effectively bridge operational characteristics with security metrics. These methodologies enable real-time, accurate predictions of system behaviors under varied operational conditions and support the optimization of control strategies. Through detailed analysis, we demonstrate how AI applications can transform traditional security assessment protocols, enhancing both the efficacy and efficiency of power system operations. The findings advocate for the potential of AI to significantly enhance the reliability and resilience of electrical grids, marking a paradigm shift towards more adaptive and intelligent power infrastructure. |
| title | Artificial Intelligence in Power System Security and Stability Analysis: A Comprehensive Review |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2408.08914 |