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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/2510.22324 |
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| _version_ | 1866915921592844288 |
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| author | Wang, Yifei Wang, Han Zhuang, Kehao Moffat, Keith Dörfler, Florian |
| author_facet | Wang, Yifei Wang, Han Zhuang, Kehao Moffat, Keith Dörfler, Florian |
| contents | The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control Wang, Yifei Wang, Han Zhuang, Kehao Moffat, Keith Dörfler, Florian Systems and Control The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach. |
| title | Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.22324 |