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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.17868 |
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| _version_ | 1866915407502245888 |
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| author | Mohamed, Amr S. Nguyen, Emily Kundur, Deepa |
| author_facet | Mohamed, Amr S. Nguyen, Emily Kundur, Deepa |
| contents | Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement learning agents for power system control applications, specifically in the context of automatic generation control. We develop the safety barriers and reinforcement learning framework necessary to establish trust in reinforcement learning as a safe option for automatic generation control - as foundation for future detailed verification and application studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe Reinforcement Learning-based Automatic Generation Control Mohamed, Amr S. Nguyen, Emily Kundur, Deepa Systems and Control Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement learning agents for power system control applications, specifically in the context of automatic generation control. We develop the safety barriers and reinforcement learning framework necessary to establish trust in reinforcement learning as a safe option for automatic generation control - as foundation for future detailed verification and application studies. |
| title | Safe Reinforcement Learning-based Automatic Generation Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.17868 |