Lipschitz-Enforced Machine Learning Framework for Accelerating Transient Stability Analysis of Networked Grid-Interactive Inverters
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916070777946112 |
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| author | Liu, Zhong Zheng, Jialin Lu, Xiaonan |
| author_facet | Liu, Zhong Zheng, Jialin Lu, Xiaonan |
| contents | The growing penetration of grid-connected inverters renders Transient Stability Analysis (TSA) increasingly challenging in modern power systems. Existing TSA methodologies encounter an intrinsic trade-off between accuracy and scalability when dealing with these networked inverter-based resources (IBRs). To bridge this gap, this paper proposes a Lipschitz-enforced machine learning framework that leverages Lipschitz continuity to restructure the transient stability certification mechanism. By replacing computationally intensive verification procedures with a deterministic and efficient algebraic check, the proposed method enables rigorous stability guarantees for complex multi-inverter systems, effectively bypassing the complexity limits of traditional analytical approximations. Validated on networked Grid-Forming (GFM) inverter systems, the proposed framework accelerates the training process by over 5 times compared to existing methods. Notably, the proposed framework substantially outperforms traditional transient stability analysis approaches (e.g., Linear Matrix Inequality and Sum-of-Squares methods) by capturing up to 30\% larger Regions of Attraction (ROA), effectively shattering the conservativeness bottleneck that has long constrained traditional analytical tools. This advancement provides a scalable and theoretically rigorous solution for the TSA of networked IBRs in modern power grids. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00883 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lipschitz-Enforced Machine Learning Framework for Accelerating Transient Stability Analysis of Networked Grid-Interactive Inverters Liu, Zhong Zheng, Jialin Lu, Xiaonan Systems and Control The growing penetration of grid-connected inverters renders Transient Stability Analysis (TSA) increasingly challenging in modern power systems. Existing TSA methodologies encounter an intrinsic trade-off between accuracy and scalability when dealing with these networked inverter-based resources (IBRs). To bridge this gap, this paper proposes a Lipschitz-enforced machine learning framework that leverages Lipschitz continuity to restructure the transient stability certification mechanism. By replacing computationally intensive verification procedures with a deterministic and efficient algebraic check, the proposed method enables rigorous stability guarantees for complex multi-inverter systems, effectively bypassing the complexity limits of traditional analytical approximations. Validated on networked Grid-Forming (GFM) inverter systems, the proposed framework accelerates the training process by over 5 times compared to existing methods. Notably, the proposed framework substantially outperforms traditional transient stability analysis approaches (e.g., Linear Matrix Inequality and Sum-of-Squares methods) by capturing up to 30\% larger Regions of Attraction (ROA), effectively shattering the conservativeness bottleneck that has long constrained traditional analytical tools. This advancement provides a scalable and theoretically rigorous solution for the TSA of networked IBRs in modern power grids. |
| title | Lipschitz-Enforced Machine Learning Framework for Accelerating Transient Stability Analysis of Networked Grid-Interactive Inverters |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2606.00883 |